cd ~
sudo apt install python3-venv
python3 -m venv ~/venvs/sensor_llm_venv --system-site-packages
source ~/venvs/sensor_llm_venv/bin/activate

cd ~
sudo apt install python3-venv
python3 -m venv ~/venvs/sensor_llm_venv --system-site-packages
source ~/venvs/sensor_llm_venv/bin/activate

source ~/venvs/sensor_llm_venv/bin/activate
cd ~
pip3 install --upgrade adafruit-python-shell
wget https://raw.githubusercontent.com/adafruit/Raspberry-Pi-Installer-Scripts/master/raspi-blinka.py
sudo -E env PATH=$PATH python3 raspi-blinka.py

source ~/venvs/sensor_llm_venv/bin/activate
cd ~
pip3 install --upgrade adafruit-python-shell
wget https://raw.githubusercontent.com/adafruit/Raspberry-Pi-Installer-Scripts/master/raspi-blinka.py
sudo -E env PATH=$PATH python3 raspi-blinka.py

# SPDX-FileCopyrightText: Copyright (c) 2025 Liz Clark for Adafruit Industries
#
# SPDX-License-Identifier: MIT

# Example usage:
import time

import board

import adafruit_sen6x

# Initialize I2C
i2c = board.I2C()

# Create SEN66 instance
sensor = adafruit_sen6x.SEN66(i2c)

# Read sensor info
print(f"Product: {sensor.product_name}")
print(f"Serial: {sensor.serial_number}")

# Check device status
status = sensor.device_status
print(f"Device {status}")

# Optional: Configure sensor before starting
# sensor.temperature_offset(offset=-2.0, slot=0)  # Apply -2°C offset
# sensor.voc_algorithm_tuning(index_offset=100)   # Adjust VOC baseline
# print(sensor.voc_algorithm) # Print VOC baseline

# CO2 configuration examples:
# sensor.co2_automatic_self_calibration = False  # Disable ASC for greenhouses
# sensor.ambient_pressure = 1020  # Set pressure in hPa
# sensor.sensor_altitude = 500    # Or set altitude in meters

# Start measurements
sensor.start_measurement()

# Wait for first measurement to be ready
print("Waiting for first measurement...")
time.sleep(2)
print("-" * 40)

# Read data continuously
while True:
    if sensor.data_ready:
        # Check for errors before reading
        sensor.check_sensor_errors()

        # Read all measurements
        data = sensor.all_measurements()

        # Display values (None = sensor still initializing)
        print(
            f"Temperature: {data['temperature']:.1f}°C"
            if data["temperature"]
            else "Temperature: initializing..."
        )
        print(
            f"Humidity: {data['humidity']:.1f}%"
            if data["humidity"]
            else "Humidity: initializing..."
        )
        print(f"PM2.5: {data['pm2_5']:.1f} µg/m³" if data["pm2_5"] else "PM2.5: initializing...")
        print(
            f"VOC Index: {data['voc_index']:.1f}"
            if data["voc_index"]
            else "VOC Index: initializing..."
        )
        print(
            f"NOx Index: {data['nox_index']:.1f}"
            if data["nox_index"]
            else "NOx Index: initializing..."
        )
        print(f"CO2: {data['co2']} ppm" if data["co2"] else "CO2: initializing...")
        print("-" * 40)
    time.sleep(2)

# SPDX-FileCopyrightText: Copyright (c) 2025 Liz Clark for Adafruit Industries
#
# SPDX-License-Identifier: MIT

# Example usage:
import time

import board

import adafruit_sen6x

# Initialize I2C
i2c = board.I2C()

# Create SEN66 instance
sensor = adafruit_sen6x.SEN66(i2c)

# Read sensor info
print(f"Product: {sensor.product_name}")
print(f"Serial: {sensor.serial_number}")

# Check device status
status = sensor.device_status
print(f"Device {status}")

# Optional: Configure sensor before starting
# sensor.temperature_offset(offset=-2.0, slot=0)  # Apply -2°C offset
# sensor.voc_algorithm_tuning(index_offset=100)   # Adjust VOC baseline
# print(sensor.voc_algorithm) # Print VOC baseline

# CO2 configuration examples:
# sensor.co2_automatic_self_calibration = False  # Disable ASC for greenhouses
# sensor.ambient_pressure = 1020  # Set pressure in hPa
# sensor.sensor_altitude = 500    # Or set altitude in meters

# Start measurements
sensor.start_measurement()

# Wait for first measurement to be ready
print("Waiting for first measurement...")
time.sleep(2)
print("-" * 40)

# Read data continuously
while True:
    if sensor.data_ready:
        # Check for errors before reading
        sensor.check_sensor_errors()

        # Read all measurements
        data = sensor.all_measurements()

        # Display values (None = sensor still initializing)
        print(
            f"Temperature: {data['temperature']:.1f}°C"
            if data["temperature"]
            else "Temperature: initializing..."
        )
        print(
            f"Humidity: {data['humidity']:.1f}%"
            if data["humidity"]
            else "Humidity: initializing..."
        )
        print(f"PM2.5: {data['pm2_5']:.1f} µg/m³" if data["pm2_5"] else "PM2.5: initializing...")
        print(
            f"VOC Index: {data['voc_index']:.1f}"
            if data["voc_index"]
            else "VOC Index: initializing..."
        )
        print(
            f"NOx Index: {data['nox_index']:.1f}"
            if data["nox_index"]
            else "NOx Index: initializing..."
        )
        print(f"CO2: {data['co2']} ppm" if data["co2"] else "CO2: initializing...")
        print("-" * 40)
    time.sleep(2)

curl -fsSL https://ollama.com/install.sh | sh

curl -fsSL https://ollama.com/install.sh | sh

ollama -h

ollama -h

source ~/venvs/sensor_llm_venv/bin/activate
pip install ollama

source ~/venvs/sensor_llm_venv/bin/activate
pip install ollama

source ~/venvs/sensor_llm_venv/bin/activate
pip install SQLAlchemy

source ~/venvs/sensor_llm_venv/bin/activate
pip install SQLAlchemy

python db_models.py

python db_models.py

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
from datetime import datetime
from sqlalchemy import Column, Integer, Float, String, DateTime, create_engine
from sqlalchemy.ext.declarative import declarative_base


Base = declarative_base()


class SensorReading(Base):
    """
    Database model for environmental sensor readings.
    """

    __tablename__ = "sensor_readings"

    id = Column(Integer, primary_key=True, autoincrement=True)
    datetime = Column(DateTime, nullable=False, default=datetime.utcnow)
    room_name = Column(String(100), nullable=False)
    temperature_c = Column(Float, nullable=True)
    temperature_f = Column(Float, nullable=True)
    humidity = Column(Float, nullable=True)  # Percentage
    pm25 = Column(Float, nullable=True)  # PM2.5 in µg/m³
    voc_index = Column(Float, nullable=True)  # VOC index
    nox_index = Column(Float, nullable=True)  # NOx index
    co2 = Column(Float, nullable=True)  # CO2 in ppm

    def __repr__(self):
        return (
            f"<SensorReading(room='{self.room_name}', "
            f"datetime='{self.datetime}', "
            f"temp_c={self.temperature_c}, "
            f"humidity={self.humidity})>"
        )


if __name__ == "__main__":

    def create_database(db_url="sqlite:///sensor_data.db"):
        """Create the database and all tables."""
        engine = create_engine(db_url, echo=True)

        # Create all tables
        Base.metadata.create_all(engine)

        print(f"Database created successfully at: {db_url}")
        return engine

    # Create the database when script is run directly
    create_database()

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
from datetime import datetime
from sqlalchemy import Column, Integer, Float, String, DateTime, create_engine
from sqlalchemy.ext.declarative import declarative_base


Base = declarative_base()


class SensorReading(Base):
    """
    Database model for environmental sensor readings.
    """

    __tablename__ = "sensor_readings"

    id = Column(Integer, primary_key=True, autoincrement=True)
    datetime = Column(DateTime, nullable=False, default=datetime.utcnow)
    room_name = Column(String(100), nullable=False)
    temperature_c = Column(Float, nullable=True)
    temperature_f = Column(Float, nullable=True)
    humidity = Column(Float, nullable=True)  # Percentage
    pm25 = Column(Float, nullable=True)  # PM2.5 in µg/m³
    voc_index = Column(Float, nullable=True)  # VOC index
    nox_index = Column(Float, nullable=True)  # NOx index
    co2 = Column(Float, nullable=True)  # CO2 in ppm

    def __repr__(self):
        return (
            f"<SensorReading(room='{self.room_name}', "
            f"datetime='{self.datetime}', "
            f"temp_c={self.temperature_c}, "
            f"humidity={self.humidity})>"
        )


if __name__ == "__main__":

    def create_database(db_url="sqlite:///sensor_data.db"):
        """Create the database and all tables."""
        engine = create_engine(db_url, echo=True)

        # Create all tables
        Base.metadata.create_all(engine)

        print(f"Database created successfully at: {db_url}")
        return engine

    # Create the database when script is run directly
    create_database()

python take_sensor_readings.py

python take_sensor_readings.py

# SPDX-FileCopyrightText: Copyright (c) 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT


import time
import board
import adafruit_sen6x
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from db_models import SensorReading


# How many seconds in between taking readings
INTERVAL = 60  # seconds

ROOM_NAME = "basement"

# Initialize I2C
i2c = board.I2C()

# Create SEN66 instance
sensor = adafruit_sen6x.SEN66(i2c)

# Read sensor info
print(f"Product: {sensor.product_name}")
print(f"Serial: {sensor.serial_number}")

# Check device status
status = sensor.device_status
print(f"Device {status}")

# Optional: Configure sensor before starting
# sensor.temperature_offset(offset=-2.0, slot=0)  # Apply -2°C offset
# sensor.voc_algorithm_tuning(index_offset=100)   # Adjust VOC baseline
# print(sensor.voc_algorithm) # Print VOC baseline

# CO2 configuration examples:
# sensor.co2_automatic_self_calibration = False  # Disable ASC for greenhouses
# sensor.ambient_pressure = 1020  # Set pressure in hPa
# sensor.sensor_altitude = 500    # Or set altitude in meters

# Start measurements
sensor.start_measurement()

# Wait for first measurement to be ready
print("Waiting for first measurement...")
time.sleep(2)
print("-" * 40)

# connect to database
engine = create_engine("sqlite:///sensor_data.db")
Session = sessionmaker(bind=engine)
session = Session()

# Read data continuously
while True:
    if sensor.data_ready:
        # Check for errors before reading
        sensor.check_sensor_errors()

        # Read all measurements
        data = sensor.all_measurements()

        # create data object instance
        reading = SensorReading(
            room_name=ROOM_NAME,
            temperature_c=data["temperature"],
            temperature_f=(data["temperature"] * 9 / 5) + 32,
            humidity=data["humidity"],
            pm25=data["pm2_5"],
            voc_index=data["voc_index"],
            nox_index=data["nox_index"],
            co2=data["co2"],
        )

        # Add and commit the reading to database
        session.add(reading)
        session.commit()
        print(reading)
        # Display values (None = sensor still initializing)
        print(
            f"Temperature: {data['temperature']:.1f}°C"
            if data["temperature"]
            else "Temperature: initializing..."
        )
        print(
            f"Humidity: {data['humidity']:.1f}%"
            if data["humidity"]
            else "Humidity: initializing..."
        )
        print(
            f"PM2.5: {data['pm2_5']:.1f} µg/m³"
            if data["pm2_5"]
            else "PM2.5: initializing..."
        )
        print(
            f"VOC Index: {data['voc_index']:.1f}"
            if data["voc_index"]
            else "VOC Index: initializing..."
        )
        print(
            f"NOx Index: {data['nox_index']:.1f}"
            if data["nox_index"]
            else "NOx Index: initializing..."
        )
        print(f"CO2: {data['co2']} ppm" if data["co2"] else "CO2: initializing...")
        print("-" * 40)
    time.sleep(INTERVAL)

# SPDX-FileCopyrightText: Copyright (c) 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT


import time
import board
import adafruit_sen6x
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from db_models import SensorReading


# How many seconds in between taking readings
INTERVAL = 60  # seconds

ROOM_NAME = "basement"

# Initialize I2C
i2c = board.I2C()

# Create SEN66 instance
sensor = adafruit_sen6x.SEN66(i2c)

# Read sensor info
print(f"Product: {sensor.product_name}")
print(f"Serial: {sensor.serial_number}")

# Check device status
status = sensor.device_status
print(f"Device {status}")

# Optional: Configure sensor before starting
# sensor.temperature_offset(offset=-2.0, slot=0)  # Apply -2°C offset
# sensor.voc_algorithm_tuning(index_offset=100)   # Adjust VOC baseline
# print(sensor.voc_algorithm) # Print VOC baseline

# CO2 configuration examples:
# sensor.co2_automatic_self_calibration = False  # Disable ASC for greenhouses
# sensor.ambient_pressure = 1020  # Set pressure in hPa
# sensor.sensor_altitude = 500    # Or set altitude in meters

# Start measurements
sensor.start_measurement()

# Wait for first measurement to be ready
print("Waiting for first measurement...")
time.sleep(2)
print("-" * 40)

# connect to database
engine = create_engine("sqlite:///sensor_data.db")
Session = sessionmaker(bind=engine)
session = Session()

# Read data continuously
while True:
    if sensor.data_ready:
        # Check for errors before reading
        sensor.check_sensor_errors()

        # Read all measurements
        data = sensor.all_measurements()

        # create data object instance
        reading = SensorReading(
            room_name=ROOM_NAME,
            temperature_c=data["temperature"],
            temperature_f=(data["temperature"] * 9 / 5) + 32,
            humidity=data["humidity"],
            pm25=data["pm2_5"],
            voc_index=data["voc_index"],
            nox_index=data["nox_index"],
            co2=data["co2"],
        )

        # Add and commit the reading to database
        session.add(reading)
        session.commit()
        print(reading)
        # Display values (None = sensor still initializing)
        print(
            f"Temperature: {data['temperature']:.1f}°C"
            if data["temperature"]
            else "Temperature: initializing..."
        )
        print(
            f"Humidity: {data['humidity']:.1f}%"
            if data["humidity"]
            else "Humidity: initializing..."
        )
        print(
            f"PM2.5: {data['pm2_5']:.1f} µg/m³"
            if data["pm2_5"]
            else "PM2.5: initializing..."
        )
        print(
            f"VOC Index: {data['voc_index']:.1f}"
            if data["voc_index"]
            else "VOC Index: initializing..."
        )
        print(
            f"NOx Index: {data['nox_index']:.1f}"
            if data["nox_index"]
            else "NOx Index: initializing..."
        )
        print(f"CO2: {data['co2']} ppm" if data["co2"] else "CO2: initializing...")
        print("-" * 40)
    time.sleep(INTERVAL)

sudo nano /etc/systemd/system/sensor-reading.service

sudo nano /etc/systemd/system/sensor-reading.service

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
[Unit]
Description=Python Script with Virtual Environment
After=network.target
Wants=network.target

[Service]
Type=simple
User=pi
Group=pi
WorkingDirectory=/home/pi/RaspberryPi_LLM_Sensor_Data
Environment=PATH=/home/pi/venvs/sensor_llm_venv/bin
ExecStart=/home/pi/RaspberryPi_LLM_Sensor_Data/start_service.sh
Restart=always
RestartSec=10

# Optional: Set environment variables
Environment=PYTHONPATH=/home/pi/RaspberryPi_LLM_Sensor_Data
Environment=PYTHONUNBUFFERED=1

# Optional: Logging
StandardOutput=journal
StandardError=journal
SyslogIdentifier=Environmental-Sensor-Reader

# Optional: Security settings
NoNewPrivileges=yes
ProtectSystem=strict
#ProtectHome=yes
ReadWritePaths=/home/pi/RaspberryPi_LLM_Sensor_Data

[Install]
WantedBy=multi-user.target

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
[Unit]
Description=Python Script with Virtual Environment
After=network.target
Wants=network.target

[Service]
Type=simple
User=pi
Group=pi
WorkingDirectory=/home/pi/RaspberryPi_LLM_Sensor_Data
Environment=PATH=/home/pi/venvs/sensor_llm_venv/bin
ExecStart=/home/pi/RaspberryPi_LLM_Sensor_Data/start_service.sh
Restart=always
RestartSec=10

# Optional: Set environment variables
Environment=PYTHONPATH=/home/pi/RaspberryPi_LLM_Sensor_Data
Environment=PYTHONUNBUFFERED=1

# Optional: Logging
StandardOutput=journal
StandardError=journal
SyslogIdentifier=Environmental-Sensor-Reader

# Optional: Security settings
NoNewPrivileges=yes
ProtectSystem=strict
#ProtectHome=yes
ReadWritePaths=/home/pi/RaspberryPi_LLM_Sensor_Data

[Install]
WantedBy=multi-user.target

#!/bin/bash
# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
cd /home/pi/RaspberryPi_LLM_Sensor_Data
source /home/pi/venvs/sensor_llm_venv/bin/activate
exec python /home/pi/RaspberryPi_LLM_Sensor_Data/take_sensor_readings.py

#!/bin/bash
# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
cd /home/pi/RaspberryPi_LLM_Sensor_Data
source /home/pi/venvs/sensor_llm_venv/bin/activate
exec python /home/pi/RaspberryPi_LLM_Sensor_Data/take_sensor_readings.py

sudo systemctl daemon-reload
sudo systemctl enable sensor-reading.service
sudo systemctl start sensor-reading.service

sudo systemctl daemon-reload
sudo systemctl enable sensor-reading.service
sudo systemctl start sensor-reading.service

journalctl -e -u sensor-reading.service

journalctl -e -u sensor-reading.service

Sep 22 08:42:31 raspberrypi your-service-name[779]: <SensorReading(room='RoomC', datetime='2025-09-22 13:42:31.173043', temp_c=20.925, humidity=65.98)>
Sep 22 08:42:31 raspberrypi your-service-name[779]: Temperature: 20.9°C
Sep 22 08:42:31 raspberrypi your-service-name[779]: Humidity: 66.0%
Sep 22 08:42:31 raspberrypi your-service-name[779]: PM2.5: 3.8 µg/m³
Sep 22 08:42:31 raspberrypi your-service-name[779]: VOC Index: 31.0
Sep 22 08:42:31 raspberrypi your-service-name[779]: NOx Index: 1.0
Sep 22 08:42:31 raspberrypi your-service-name[779]: CO2: 422.0 ppm
Sep 22 08:42:31 raspberrypi your-service-name[779]: ----------------------------------------
Sep 22 08:43:31 raspberrypi your-service-name[779]: <SensorReading(room='RoomC', datetime='2025-09-22 13:43:31.309236', temp_c=20.925, humidity=65.98)>
Sep 22 08:43:31 raspberrypi your-service-name[779]: Temperature: 20.9°C
Sep 22 08:43:31 raspberrypi your-service-name[779]: Humidity: 66.0%
Sep 22 08:43:31 raspberrypi your-service-name[779]: PM2.5: 3.8 µg/m³
Sep 22 08:43:31 raspberrypi your-service-name[779]: VOC Index: 31.0
Sep 22 08:43:31 raspberrypi your-service-name[779]: NOx Index: 1.0
Sep 22 08:43:31 raspberrypi your-service-name[779]: CO2: 421.0 ppm

Sep 22 08:42:31 raspberrypi your-service-name[779]: <SensorReading(room='RoomC', datetime='2025-09-22 13:42:31.173043', temp_c=20.925, humidity=65.98)>
Sep 22 08:42:31 raspberrypi your-service-name[779]: Temperature: 20.9°C
Sep 22 08:42:31 raspberrypi your-service-name[779]: Humidity: 66.0%
Sep 22 08:42:31 raspberrypi your-service-name[779]: PM2.5: 3.8 µg/m³
Sep 22 08:42:31 raspberrypi your-service-name[779]: VOC Index: 31.0
Sep 22 08:42:31 raspberrypi your-service-name[779]: NOx Index: 1.0
Sep 22 08:42:31 raspberrypi your-service-name[779]: CO2: 422.0 ppm
Sep 22 08:42:31 raspberrypi your-service-name[779]: ----------------------------------------
Sep 22 08:43:31 raspberrypi your-service-name[779]: <SensorReading(room='RoomC', datetime='2025-09-22 13:43:31.309236', temp_c=20.925, humidity=65.98)>
Sep 22 08:43:31 raspberrypi your-service-name[779]: Temperature: 20.9°C
Sep 22 08:43:31 raspberrypi your-service-name[779]: Humidity: 66.0%
Sep 22 08:43:31 raspberrypi your-service-name[779]: PM2.5: 3.8 µg/m³
Sep 22 08:43:31 raspberrypi your-service-name[779]: VOC Index: 31.0
Sep 22 08:43:31 raspberrypi your-service-name[779]: NOx Index: 1.0
Sep 22 08:43:31 raspberrypi your-service-name[779]: CO2: 421.0 ppm

python prompt_llm_for_summary.py

python prompt_llm_for_summary.py

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
import csv
from datetime import datetime, timedelta, UTC
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from ollama import chat
from db_models import SensorReading

# pylint: disable=too-many-locals, too-many-nested-blocks

# Database connection configuration
DATABASE_URL = "sqlite:///sensor_data.db"

MODEL = "gemma3:1b"

# Room name to pull data for. Update to match one of your rooms.
ROOM = "Basement"

# Specify a Custom Date Range
# QUERY_START = datetime(2025, 9, 16, 0, 0, 0, tzinfo=UTC)
# QUERY_END = datetime(2025, 9, 18, 19, 0, 0, tzinfo=UTC)

# Defaults to last 24 hours if start and end are None
QUERY_START = None
QUERY_END = None

# Time interval in minutes to export data with i.e. one data point every 30 minutes.
SAMPLE_RATE = 30  # minutes


PROMPT = """Analyze the following environmental sensor data. Provide a summary of its content,
 identify key patterns or insights, and suggest potential further analysis or questions based on this data.

Data:
---
%%_DATA_PLACEHOLDER_%%
---

The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Please summarize the data, identify key patterns, insights, or trends.
"""


def fetch_data(
    room, start_datetime=None, end_datetime=None, output_file=None, sample_rate=30
):
    """
    Fetch all SensorReading records from a specified time range for RoomC
    and save them to a CSV file

    Args:
        room (str): Room name
        start_datetime (Optional[datetime]): Start of the time range (default: 24 hours ago)
        end_datetime (Optional[datetime]): End of the time range (default: now)
        output_file (str): Name of the CSV file to create
        sample_rate (int): Sampling interval in minutes (e.g., 5 for every 5 minutes)
    """
    # Create database engine and session
    engine = create_engine(DATABASE_URL)
    Session = sessionmaker(bind=engine)
    session = Session()

    try:
        # Set default values if not provided
        if end_datetime is None:
            end_datetime = datetime.now(UTC)
        if start_datetime is None:
            start_datetime = end_datetime - timedelta(hours=24)

        # Ensure start_datetime is before end_datetime
        if start_datetime >= end_datetime:
            raise ValueError("start_datetime must be before end_datetime")

        print(f"Fetching data for {room} from {start_datetime} to {end_datetime}")

        # Query for RoomC records within the specified time range
        query = (
            session.query(SensorReading)
            .filter(
                SensorReading.room_name == room,
                SensorReading.datetime >= start_datetime,
                SensorReading.datetime <= end_datetime,
            )
            .order_by(SensorReading.datetime.desc())
        )

        # Execute the query
        results = query.all()

        # Apply sampling if sample_rate > 1
        if sample_rate > 1:
            sampled_results = []
            if results:
                # Start from the most recent record (first in desc order)
                base_time = results[0].datetime

                for reading in results:
                    # Calculate minutes difference from the base time
                    time_diff = abs((base_time - reading.datetime).total_seconds() / 60)

                    # Include reading if it falls on a sample interval
                    if time_diff % sample_rate < 1:  # Allow 1 minute tolerance
                        sampled_results.append(reading)

            results = sampled_results
            print(
                f"Applied {sample_rate}-minute sampling: {len(results)} records selected"
            )

        if output_file is not None:
            # Write results to CSV file
            with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
                fieldnames = [
                    "datetime",
                    "temperature_f",
                    "humidity",
                    "pm25",
                    "voc_index",
                    "nox_index",
                    "co2",
                ]
                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

                # Write header
                writer.writeheader()

                # Write data rows
                for reading in results:
                    writer.writerow(
                        {
                            "datetime": (
                                reading.datetime.isoformat()
                                if reading.datetime
                                else None
                            ),
                            "temperature_f": reading.temperature_f,
                            "humidity": reading.humidity,
                            "pm25": reading.pm25,
                            "voc_index": reading.voc_index,
                            "nox_index": reading.nox_index,
                            "co2": reading.co2,
                        }
                    )

            time_range = end_datetime - start_datetime
            print(
                f"Successfully saved {len(results)} records for RoomC to '{output_file}'"
                + f" (time range: {time_range})"
                + (f" (sampled every {sample_rate} minutes)" if sample_rate > 1 else "")
            )
        return results
    # pylint:disable=broad-except
    except Exception as e:
        print(f"Error fetching data: {e}")
        return []

    finally:
        # Always close the session
        session.close()


if __name__ == "__main__":

    records = fetch_data(
        room=ROOM,
        start_datetime=QUERY_START,
        end_datetime=QUERY_END,
        sample_rate=30,
        output_file="sensor_data.csv",
    )

    with open("sensor_data.csv", "r") as f:
        csv_data = f.read()

    stream = chat(
        model=MODEL,
        messages=[
            {
                "role": "user",
                "content": PROMPT.replace("%%_DATA_PLACEHOLDER_%%", csv_data),
            },
        ],
        stream=True,
    )

    for chunk in stream:
        print(chunk["message"]["content"], end="", flush=True)

# SPDX-FileCopyrightText: 2025 Tim Cocks for Adafruit Industries
#
# SPDX-License-Identifier: MIT
import csv
from datetime import datetime, timedelta, UTC
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from ollama import chat
from db_models import SensorReading

# pylint: disable=too-many-locals, too-many-nested-blocks

# Database connection configuration
DATABASE_URL = "sqlite:///sensor_data.db"

MODEL = "gemma3:1b"

# Room name to pull data for. Update to match one of your rooms.
ROOM = "Basement"

# Specify a Custom Date Range
# QUERY_START = datetime(2025, 9, 16, 0, 0, 0, tzinfo=UTC)
# QUERY_END = datetime(2025, 9, 18, 19, 0, 0, tzinfo=UTC)

# Defaults to last 24 hours if start and end are None
QUERY_START = None
QUERY_END = None

# Time interval in minutes to export data with i.e. one data point every 30 minutes.
SAMPLE_RATE = 30  # minutes


PROMPT = """Analyze the following environmental sensor data. Provide a summary of its content,
 identify key patterns or insights, and suggest potential further analysis or questions based on this data.

Data:
---
%%_DATA_PLACEHOLDER_%%
---

The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Please summarize the data, identify key patterns, insights, or trends.
"""


def fetch_data(
    room, start_datetime=None, end_datetime=None, output_file=None, sample_rate=30
):
    """
    Fetch all SensorReading records from a specified time range for RoomC
    and save them to a CSV file

    Args:
        room (str): Room name
        start_datetime (Optional[datetime]): Start of the time range (default: 24 hours ago)
        end_datetime (Optional[datetime]): End of the time range (default: now)
        output_file (str): Name of the CSV file to create
        sample_rate (int): Sampling interval in minutes (e.g., 5 for every 5 minutes)
    """
    # Create database engine and session
    engine = create_engine(DATABASE_URL)
    Session = sessionmaker(bind=engine)
    session = Session()

    try:
        # Set default values if not provided
        if end_datetime is None:
            end_datetime = datetime.now(UTC)
        if start_datetime is None:
            start_datetime = end_datetime - timedelta(hours=24)

        # Ensure start_datetime is before end_datetime
        if start_datetime >= end_datetime:
            raise ValueError("start_datetime must be before end_datetime")

        print(f"Fetching data for {room} from {start_datetime} to {end_datetime}")

        # Query for RoomC records within the specified time range
        query = (
            session.query(SensorReading)
            .filter(
                SensorReading.room_name == room,
                SensorReading.datetime >= start_datetime,
                SensorReading.datetime <= end_datetime,
            )
            .order_by(SensorReading.datetime.desc())
        )

        # Execute the query
        results = query.all()

        # Apply sampling if sample_rate > 1
        if sample_rate > 1:
            sampled_results = []
            if results:
                # Start from the most recent record (first in desc order)
                base_time = results[0].datetime

                for reading in results:
                    # Calculate minutes difference from the base time
                    time_diff = abs((base_time - reading.datetime).total_seconds() / 60)

                    # Include reading if it falls on a sample interval
                    if time_diff % sample_rate < 1:  # Allow 1 minute tolerance
                        sampled_results.append(reading)

            results = sampled_results
            print(
                f"Applied {sample_rate}-minute sampling: {len(results)} records selected"
            )

        if output_file is not None:
            # Write results to CSV file
            with open(output_file, "w", newline="", encoding="utf-8") as csvfile:
                fieldnames = [
                    "datetime",
                    "temperature_f",
                    "humidity",
                    "pm25",
                    "voc_index",
                    "nox_index",
                    "co2",
                ]
                writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

                # Write header
                writer.writeheader()

                # Write data rows
                for reading in results:
                    writer.writerow(
                        {
                            "datetime": (
                                reading.datetime.isoformat()
                                if reading.datetime
                                else None
                            ),
                            "temperature_f": reading.temperature_f,
                            "humidity": reading.humidity,
                            "pm25": reading.pm25,
                            "voc_index": reading.voc_index,
                            "nox_index": reading.nox_index,
                            "co2": reading.co2,
                        }
                    )

            time_range = end_datetime - start_datetime
            print(
                f"Successfully saved {len(results)} records for RoomC to '{output_file}'"
                + f" (time range: {time_range})"
                + (f" (sampled every {sample_rate} minutes)" if sample_rate > 1 else "")
            )
        return results
    # pylint:disable=broad-except
    except Exception as e:
        print(f"Error fetching data: {e}")
        return []

    finally:
        # Always close the session
        session.close()


if __name__ == "__main__":

    records = fetch_data(
        room=ROOM,
        start_datetime=QUERY_START,
        end_datetime=QUERY_END,
        sample_rate=30,
        output_file="sensor_data.csv",
    )

    with open("sensor_data.csv", "r") as f:
        csv_data = f.read()

    stream = chat(
        model=MODEL,
        messages=[
            {
                "role": "user",
                "content": PROMPT.replace("%%_DATA_PLACEHOLDER_%%", csv_data),
            },
        ],
        stream=True,
    )

    for chunk in stream:
        print(chunk["message"]["content"], end="", flush=True)

Analyze the following environmental sensor data. Provide a summary of its content, 
identify key patterns or insights, and suggest potential further analysis or questions based on this data.
The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Data:
---
{csv_data}
---

Analyze the following environmental sensor data. Provide a summary of its content, 
identify key patterns or insights, and suggest potential further analysis or questions based on this data.
The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Data:
---
{csv_data}
---

Okay, let's analyze the data you provided.  It looks like you're giving me a sequence of time intervals, likely representing measurements of some kind.  Here’s a breakdown of what we can observe:

**Observations & Potential Interpretation**

1.  **Time Intervals:** The data consists of `[time_interval1, time_interval2, ...]`. The `time_interval` seems to be in seconds.

2.  **Values:** Each `time_interval` contains a numerical value. It looks like the values are relatively small and consistent, indicating a measure of some kind of phenomenon.

3.  **Possible Interpretations (Based on the Context):**

    *   **Sensor Readings:** The most probable interpretation is that these values represent readings from sensors.  Each interval could represent a particular time point and associated measurement.
    *   **Process Tracking:** These intervals could be intervals during a process. For example, if the intervals represent times when a certain event occurs,  each measurement might be related to the event.
    *   **Data Points:** They could represent a series of data points collected over time.

**To help me understand this better and potentially provide more useful insights, could you tell me:**

*   **What kind of data is this?** (e.g., temperature readings, wind speed, pressure, light intensity, etc.)?
*   **What is the context of this data?** (e.g., what is being measured, what is the purpose of collecting this data?)

Okay, let's analyze the data you provided.  It looks like you're giving me a sequence of time intervals, likely representing measurements of some kind.  Here’s a breakdown of what we can observe:

**Observations & Potential Interpretation**

1.  **Time Intervals:** The data consists of `[time_interval1, time_interval2, ...]`. The `time_interval` seems to be in seconds.

2.  **Values:** Each `time_interval` contains a numerical value. It looks like the values are relatively small and consistent, indicating a measure of some kind of phenomenon.

3.  **Possible Interpretations (Based on the Context):**

    *   **Sensor Readings:** The most probable interpretation is that these values represent readings from sensors.  Each interval could represent a particular time point and associated measurement.
    *   **Process Tracking:** These intervals could be intervals during a process. For example, if the intervals represent times when a certain event occurs,  each measurement might be related to the event.
    *   **Data Points:** They could represent a series of data points collected over time.

**To help me understand this better and potentially provide more useful insights, could you tell me:**

*   **What kind of data is this?** (e.g., temperature readings, wind speed, pressure, light intensity, etc.)?
*   **What is the context of this data?** (e.g., what is being measured, what is the purpose of collecting this data?)

<think>
Okay, let me try to figure out what the user is asking here. They provided a long text that seems to be a series of data points or some kind of structured information. The user's query is to "Analyze the data and provide some insights." But the text is a mix of dates, times, numbers, and maybe some patterns. 

First, I need to check if there's any specific analysis they want. The text looks like a list of entries, each starting with a date and time, followed by some numerical values. Maybe they're looking to find trends, correlations, or patterns in the data over time. Since the user mentioned "analyze," I should ask them to clarify what kind of analysis they need, like correlation, trends, or anything else.

But wait, the original text is in a format that's hard to parse. Each line seems to have a date, time, and some values. Maybe they're providing a dataset that needs analysis. However, without more context, it's hard to tell. The user might be a student or researcher needing to process this data for a report, exam, or project. They might need help interpreting the data, such as checking for anomalies, identifying trends, or correlating variables.

I should respond by asking them to specify the type of analysis they want, like correlation, time trends, or anything else. Also, offer to help with any specific aspects they have in mind. That way, I can provide the necessary assistance based on their needs.
</think>

To help you analyze the data, could you clarify what specific insights you need? For example:
- Are you looking for trends over time?
- Are you trying to identify patterns or correlations?
- Do you want to check for anomalies or correlations with other variables?

Let me know, and I'll assist accordingly!

<think>
Okay, let me try to figure out what the user is asking here. They provided a long text that seems to be a series of data points or some kind of structured information. The user's query is to "Analyze the data and provide some insights." But the text is a mix of dates, times, numbers, and maybe some patterns. 

First, I need to check if there's any specific analysis they want. The text looks like a list of entries, each starting with a date and time, followed by some numerical values. Maybe they're looking to find trends, correlations, or patterns in the data over time. Since the user mentioned "analyze," I should ask them to clarify what kind of analysis they need, like correlation, trends, or anything else.

But wait, the original text is in a format that's hard to parse. Each line seems to have a date, time, and some values. Maybe they're providing a dataset that needs analysis. However, without more context, it's hard to tell. The user might be a student or researcher needing to process this data for a report, exam, or project. They might need help interpreting the data, such as checking for anomalies, identifying trends, or correlating variables.

I should respond by asking them to specify the type of analysis they want, like correlation, time trends, or anything else. Also, offer to help with any specific aspects they have in mind. That way, I can provide the necessary assistance based on their needs.
</think>

To help you analyze the data, could you clarify what specific insights you need? For example:
- Are you looking for trends over time?
- Are you trying to identify patterns or correlations?
- Do you want to check for anomalies or correlations with other variables?

Let me know, and I'll assist accordingly!

<think>
Okay, let's see what I need to do here. The user provided a dataset of temperatures recorded over several days and wants me to analyze it. They mentioned looking for patterns or trends in the data.

First, I'll start by understanding the structure of the data. Each row represents a day with specific temperature readings at different times. There are columns for date (formatted as "YYYY-MM-DD"), time (in 24-hour format), and temperature (in degrees Celsius). 

Looking at the dates, they seem to be in chronological order from January 1st to December 31st of some year. The temperatures vary each day, so I need to check if there's a consistent pattern or if certain days have higher/lower temperatures.

I notice that on most days, the temperature peaks around midday (e.g., 12:00 PM) and then decreases in the evening. For example, on January 1st, the highest temperature was recorded at noon with 61°C, followed by a drop to 58°C at midnight. Similarly, other days show similar patterns where temperatures are higher during the day and cooler at night.

Another observation is that there might be some variation between weekdays (Monday to Friday) and weekends (Saturday and Sunday). For instance, on Monday, the highest temperature was recorded in the morning with 60°C, while on Saturday it was around noon. This suggests that maybe the data collection method or environmental factors change slightly during these periods.

I also see that there are some days where temperatures don't follow a clear pattern, like January 3rd and January 5th, which have lower temperature readings at different times. These might be anomalies or unusual weather conditions for this location.

To further analyze the data, I could calculate the average daily maximum and minimum temperatures to understand the overall range of temperatures over time. Additionally, plotting these temperatures against date or time could help visualize any trends or seasonal patterns.

Another approach is to check if there are any correlations between temperature readings at different times of day. For example, does a higher temperature in the morning correlate with lower temperatures later in the day? Or vice versa?

I should also consider possible external factors that might influence the temperature readings, such as weather events (e.g., storms), changes in local geography affecting wind patterns, or shifts in human activity levels.

In summary, the key steps would be to:

1. Analyze daily maximum and minimum temperatures.
2. Plot temperature data over time to identify seasonal trends.
3. Examine variations between weekdays and weekends.
4. Investigate correlations between different times of day.
5. Consider external factors affecting temperature readings.
6. Identify any anomalies or unusual patterns in the data.

By following these steps, I can gain insights into the temperature behavior at this location over time and determine if there are consistent trends or if certain days have unique characteristics.
</think>

### Analysis of Temperature Data (January 1 to December 31)

#### **Overview**
The dataset contains daily temperature records from January 1 to December 31, with readings recorded every hour. The data spans a full year, providing insights into seasonal and diurnal patterns.

---

#### **Key Observations**

1. **Diurnal Cycle**:
   - Temperatures typically peak in the afternoon (12:00 PM) and drop at night.
   - Example: On January 28th, temperatures reached 61°C at noon before dropping to 58°C by midnight.

2. **Seasonal Trends**:
   - Higher maximum temperatures are observed during spring and summer months (March–August).
   - Lower maximum temperatures occur in autumn and winter (September–December), with the lowest readings typically occurring overnight.
   - Example: On January 1st, the highest temperature was 60°C at noon, while on December 31st, it was 58°C.

3. **Weekday vs. Weekend Differences**:
   - Weekdays often show higher maximum temperatures in the morning (e.g., 61°C on a Monday).
   - Weekends tend to have lower maxima later in the day (e.g., 59°C on a Saturday at noon).

4. **Anomalies and Variability**:
   - Some days, such as January 3rd or December 5th, exhibit unusual temperature patterns without clear diurnal variation.
   - Days like January 28th show significant fluctuations between morning and evening readings.

---

#### **Statistical Summary**

- **Maximum Temperatures (°C)**:
  - Average: ~58.0°C
  - Range: 46–61°C

- **Minimum Temperatures (°C)**:
  - Average: ~45.5°C
  - Range: 40–62°C

---

#### **Correlation Analysis**

- **Time of Day**:
  - Maximum temperatures are most consistently observed in the afternoon.
  - Minimum temperatures occur overnight, with some exceptions.

- **Seasonal Correlations**:
  - Higher maximums correlate with warmer months (March–August).
  - Lower maxima and minimums align with cooler seasons (September–December).

---

#### **External Factors**

- **Human Activity**: No apparent impact from human activity on temperature readings.
- **Weather Events**: No storms or extreme weather events evident in the data.
- **Geographic Features**: The location appears to be a flat, open area without significant topography affecting wind patterns.

---

#### **Visualizations**
1. **Daily Temperature Plots**:
   - Show peaks at noon and valleys overnight, with seasonal shifts.
2. **Seasonal Trends**:
   - Maximum temperatures increase from January (46°C) to August (62°C), then decrease gradually.
3. **Weekday/Weekend Comparison**:
   - Weekdays have slightly higher maxima in the morning; weekends show lower maxima later.

---

#### **Conclusion**
The data reveals a clear diurnal and seasonal pattern, with temperatures peaking during the day and dropping at night. There is no evidence of significant human impact or external weather events affecting the readings. The temperature range spans 15°C (46–61°C), indicating moderate climate variability for this location.

**Recommendations**:
- Continue monitoring to identify long-term trends.
- Investigate potential causes for anomalies like January 3rd and December 5th.
- Explore correlations between different times of day or seasonal shifts.

<think>
Okay, let's see what I need to do here. The user provided a dataset of temperatures recorded over several days and wants me to analyze it. They mentioned looking for patterns or trends in the data.

First, I'll start by understanding the structure of the data. Each row represents a day with specific temperature readings at different times. There are columns for date (formatted as "YYYY-MM-DD"), time (in 24-hour format), and temperature (in degrees Celsius). 

Looking at the dates, they seem to be in chronological order from January 1st to December 31st of some year. The temperatures vary each day, so I need to check if there's a consistent pattern or if certain days have higher/lower temperatures.

I notice that on most days, the temperature peaks around midday (e.g., 12:00 PM) and then decreases in the evening. For example, on January 1st, the highest temperature was recorded at noon with 61°C, followed by a drop to 58°C at midnight. Similarly, other days show similar patterns where temperatures are higher during the day and cooler at night.

Another observation is that there might be some variation between weekdays (Monday to Friday) and weekends (Saturday and Sunday). For instance, on Monday, the highest temperature was recorded in the morning with 60°C, while on Saturday it was around noon. This suggests that maybe the data collection method or environmental factors change slightly during these periods.

I also see that there are some days where temperatures don't follow a clear pattern, like January 3rd and January 5th, which have lower temperature readings at different times. These might be anomalies or unusual weather conditions for this location.

To further analyze the data, I could calculate the average daily maximum and minimum temperatures to understand the overall range of temperatures over time. Additionally, plotting these temperatures against date or time could help visualize any trends or seasonal patterns.

Another approach is to check if there are any correlations between temperature readings at different times of day. For example, does a higher temperature in the morning correlate with lower temperatures later in the day? Or vice versa?

I should also consider possible external factors that might influence the temperature readings, such as weather events (e.g., storms), changes in local geography affecting wind patterns, or shifts in human activity levels.

In summary, the key steps would be to:

1. Analyze daily maximum and minimum temperatures.
2. Plot temperature data over time to identify seasonal trends.
3. Examine variations between weekdays and weekends.
4. Investigate correlations between different times of day.
5. Consider external factors affecting temperature readings.
6. Identify any anomalies or unusual patterns in the data.

By following these steps, I can gain insights into the temperature behavior at this location over time and determine if there are consistent trends or if certain days have unique characteristics.
</think>

### Analysis of Temperature Data (January 1 to December 31)

#### **Overview**
The dataset contains daily temperature records from January 1 to December 31, with readings recorded every hour. The data spans a full year, providing insights into seasonal and diurnal patterns.

---

#### **Key Observations**

1. **Diurnal Cycle**:
   - Temperatures typically peak in the afternoon (12:00 PM) and drop at night.
   - Example: On January 28th, temperatures reached 61°C at noon before dropping to 58°C by midnight.

2. **Seasonal Trends**:
   - Higher maximum temperatures are observed during spring and summer months (March–August).
   - Lower maximum temperatures occur in autumn and winter (September–December), with the lowest readings typically occurring overnight.
   - Example: On January 1st, the highest temperature was 60°C at noon, while on December 31st, it was 58°C.

3. **Weekday vs. Weekend Differences**:
   - Weekdays often show higher maximum temperatures in the morning (e.g., 61°C on a Monday).
   - Weekends tend to have lower maxima later in the day (e.g., 59°C on a Saturday at noon).

4. **Anomalies and Variability**:
   - Some days, such as January 3rd or December 5th, exhibit unusual temperature patterns without clear diurnal variation.
   - Days like January 28th show significant fluctuations between morning and evening readings.

---

#### **Statistical Summary**

- **Maximum Temperatures (°C)**:
  - Average: ~58.0°C
  - Range: 46–61°C

- **Minimum Temperatures (°C)**:
  - Average: ~45.5°C
  - Range: 40–62°C

---

#### **Correlation Analysis**

- **Time of Day**:
  - Maximum temperatures are most consistently observed in the afternoon.
  - Minimum temperatures occur overnight, with some exceptions.

- **Seasonal Correlations**:
  - Higher maximums correlate with warmer months (March–August).
  - Lower maxima and minimums align with cooler seasons (September–December).

---

#### **External Factors**

- **Human Activity**: No apparent impact from human activity on temperature readings.
- **Weather Events**: No storms or extreme weather events evident in the data.
- **Geographic Features**: The location appears to be a flat, open area without significant topography affecting wind patterns.

---

#### **Visualizations**
1. **Daily Temperature Plots**:
   - Show peaks at noon and valleys overnight, with seasonal shifts.
2. **Seasonal Trends**:
   - Maximum temperatures increase from January (46°C) to August (62°C), then decrease gradually.
3. **Weekday/Weekend Comparison**:
   - Weekdays have slightly higher maxima in the morning; weekends show lower maxima later.

---

#### **Conclusion**
The data reveals a clear diurnal and seasonal pattern, with temperatures peaking during the day and dropping at night. There is no evidence of significant human impact or external weather events affecting the readings. The temperature range spans 15°C (46–61°C), indicating moderate climate variability for this location.

**Recommendations**:
- Continue monitoring to identify long-term trends.
- Investigate potential causes for anomalies like January 3rd and December 5th.
- Explore correlations between different times of day or seasonal shifts.

Analyze the following environmental sensor data. Provide a summary of its content, 
identify key patterns or insights, and suggest potential further analysis or questions based on this data.

Data:
---
{csv_data}
---

The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Please summarize the data, identify key patterns, insights, or trends.

Analyze the following environmental sensor data. Provide a summary of its content, 
identify key patterns or insights, and suggest potential further analysis or questions based on this data.

Data:
---
{csv_data}
---

The data fields are:
- UTC Datetime
- Temperature in degrees F
- Humidity percent
- pm2.5 in µg/m³
- VOC index 
- NOx index
- CO2 in ppm

Please summarize the data, identify key patterns, insights, or trends.

<think>
Okay, let me try to figure out what the user is asking here. They provided a dataset with multiple columns: UTC datetime, temperature, humidity, PM2.5, VOC index, NOx, and CO2. The task is to summarize the data, identify key patterns, and maybe find trends or insights.

First, I need to look at the data fields. The UTC datetime seems to be the time when the data is collected. The temperature is in Fahrenheit, so that's a temperature measurement. Humidity is a percentage, so that's a humidity level. PM2.5 is in micrograms per cubic meter, which is a pollution index. VOC and NOx are also given, along with CO2. 

Now, summarizing the data. Maybe I should check if there's any obvious patterns. For example, if PM2.5 is increasing over time, that would be a trend. Or if there's a correlation between VOC and NOx, that could be a key pattern. Also, looking at the CO2 levels, maybe they are increasing, which could be important for environmental monitoring.

Looking at the VOC index. If that's a pollutant, maybe there's a trend in concentration. Similarly, NOx could indicate air quality. CO2 levels might be a good indicator of climate change. 

Wait, the user said to identify patterns and insights. So maybe I should mention that there's a correlation between PM2.5 and CO2, which could be related to climate change. Also, the VOC index could show pollution levels. For the NOx, that's nitrogen oxides, which are important pollutants. 

I should also check if there are any anomalies or outliers. For example, if PM2.5 is unusually high, that might indicate a problem. But since the user is asking for patterns, maybe I should focus on the trends without outliers. 

Another thing to consider is the time frame. The data starts from 2025-09-16 and goes up to the latest date. So maybe the data is collected over time, and there's a growing trend in CO2. Also, the PM2.5 could be increasing, which might be linked to climate change. 

I need to make sure I'm not missing any key patterns. For example, if the VOC index is increasing, that could mean pollution is rising. Also, NOx and CO2 levels might be indicating that air quality is deteriorating. 

Putting it all together, the summary would highlight these trends: increasing CO2, rising PM2.5, increasing VOC and NOx levels, and potential correlations with air quality and climate change. That should cover the user's request.
</think>

Here's a summary of the data:

### Key Patterns and Insights:
1. **CO2 Trends**:  
   - CO2 levels are consistently increasing over time, indicating a growing climate impact. This trend suggests potential environmental changes, though precise timing and scale are not fully captured.

2. **PM2.5 and Climate Change**:  
   - PM2.5 values (micrograms/m³) are rising, which aligns with climate change drivers like increased industrial emissions. This could highlight urbanization or technological changes affecting air quality.

3. **Pollution Indicators**:  
   - **VOC Index**: Increasing pollution levels reflect elevated anthropogenic emissions, indicating urbanization or industrial activity.  
   - **NOx Index**: Elevated NOx levels (nitrogen dioxide) further underscore air quality deterioration, likely due to vehicle emissions or industrial processes.

4. **Temporal Trends**:  
   - The data spans from 2025-09-16 to the latest, showing a gradual progression of pollution metrics. While some patterns (e.g., CO2 growth) are consistent, others (e.g., PM2.5) may require further data resolution for clarity.

### Summary:
The data highlights a progression in air quality pollutants, with rising CO2 and PM2.5 levels linked to climate change. VOC and NOx trends suggest environmental degradation, while CO2 trends point to climate impacts. These patterns indicate a possible link between pollution and environmental change, though more detailed data may refine the conclusions.

<think>
Okay, let me try to figure out what the user is asking here. They provided a dataset with multiple columns: UTC datetime, temperature, humidity, PM2.5, VOC index, NOx, and CO2. The task is to summarize the data, identify key patterns, and maybe find trends or insights.

First, I need to look at the data fields. The UTC datetime seems to be the time when the data is collected. The temperature is in Fahrenheit, so that's a temperature measurement. Humidity is a percentage, so that's a humidity level. PM2.5 is in micrograms per cubic meter, which is a pollution index. VOC and NOx are also given, along with CO2. 

Now, summarizing the data. Maybe I should check if there's any obvious patterns. For example, if PM2.5 is increasing over time, that would be a trend. Or if there's a correlation between VOC and NOx, that could be a key pattern. Also, looking at the CO2 levels, maybe they are increasing, which could be important for environmental monitoring.

Looking at the VOC index. If that's a pollutant, maybe there's a trend in concentration. Similarly, NOx could indicate air quality. CO2 levels might be a good indicator of climate change. 

Wait, the user said to identify patterns and insights. So maybe I should mention that there's a correlation between PM2.5 and CO2, which could be related to climate change. Also, the VOC index could show pollution levels. For the NOx, that's nitrogen oxides, which are important pollutants. 

I should also check if there are any anomalies or outliers. For example, if PM2.5 is unusually high, that might indicate a problem. But since the user is asking for patterns, maybe I should focus on the trends without outliers. 

Another thing to consider is the time frame. The data starts from 2025-09-16 and goes up to the latest date. So maybe the data is collected over time, and there's a growing trend in CO2. Also, the PM2.5 could be increasing, which might be linked to climate change. 

I need to make sure I'm not missing any key patterns. For example, if the VOC index is increasing, that could mean pollution is rising. Also, NOx and CO2 levels might be indicating that air quality is deteriorating. 

Putting it all together, the summary would highlight these trends: increasing CO2, rising PM2.5, increasing VOC and NOx levels, and potential correlations with air quality and climate change. That should cover the user's request.
</think>

Here's a summary of the data:

### Key Patterns and Insights:
1. **CO2 Trends**:  
   - CO2 levels are consistently increasing over time, indicating a growing climate impact. This trend suggests potential environmental changes, though precise timing and scale are not fully captured.

2. **PM2.5 and Climate Change**:  
   - PM2.5 values (micrograms/m³) are rising, which aligns with climate change drivers like increased industrial emissions. This could highlight urbanization or technological changes affecting air quality.

3. **Pollution Indicators**:  
   - **VOC Index**: Increasing pollution levels reflect elevated anthropogenic emissions, indicating urbanization or industrial activity.  
   - **NOx Index**: Elevated NOx levels (nitrogen dioxide) further underscore air quality deterioration, likely due to vehicle emissions or industrial processes.

4. **Temporal Trends**:  
   - The data spans from 2025-09-16 to the latest, showing a gradual progression of pollution metrics. While some patterns (e.g., CO2 growth) are consistent, others (e.g., PM2.5) may require further data resolution for clarity.

### Summary:
The data highlights a progression in air quality pollutants, with rising CO2 and PM2.5 levels linked to climate change. VOC and NOx trends suggest environmental degradation, while CO2 trends point to climate impacts. These patterns indicate a possible link between pollution and environmental change, though more detailed data may refine the conclusions.

Okay, here's a summary and analysis of the provided data, broken down into key observations:

**1. Overall Trends:**

* **Temperature & Humidity Correlation:** There’s a strong positive correlation between temperature and humidity.  As temperature increases, humidity generally increases. This is a fairly consistent pattern across the dataset.
* **VOC & CO2 Spike:**  There's a noticeable spike in VOC (Volatile Organic Compounds) and CO2 levels, especially around the evening/night hours. This suggests potential events or conditions contributing to these increases.  The magnitude of these spikes varies significantly.
* **Time of Day Variations:**  The data seems to have diurnal variations, with higher values generally occurring during the day (especially after the 12:00 PM mark). There’s a very slight (and perhaps subtle) increase in the evening hours.

**2. Specific Observations & Patterns:**

* **Temperature and Humidity Peaks:**  The data shows periods of high temperatures and humidity, frequently within a few hours of each other.  This suggests a period of elevated environmental conditions.
* **VOC Spike - Timing is Key:** The most significant increase in VOC is concentrated between 12:00 PM and 6:00 PM. This strongly indicates a potential link between increased outdoor activity and increased emissions.
* **Temperature and Humidity Correlation – Consistent Trend:** The temperature and humidity fluctuate around a relatively stable baseline (around 22-28°C), suggesting a thermal equilibrium.

**3. Potential Insights & Questions:**

* **Event Correlation:** Given the spike in VOC and temperature during the day, we could investigate if there’s a link between these conditions and specific events (e.g., increased traffic, construction, outdoor gatherings).
* **Climate Influence:**  The correlations suggest a potential influence of climate patterns on air quality.  Long-term trends in temperature and humidity could be driving these observations.
* **Source of VOC:** Understanding the sources of the VOC is critical.  Are these from industrial emissions, vehicle exhaust, natural sources (vegetation, soil), or something else?
* **Spatial Variation:**  While the data is spread out, it’s important to understand *where* the peaks are occurring.  Are they localized to certain areas or regions?

**4.  Recommendations for Further Analysis**

* **Time-Series Analysis:**  A time-series analysis would help reveal underlying patterns and trends in the data.
* **Correlation Analysis:**  Statistical correlation analysis could be done to understand the strength and significance of the relationships between temperature, humidity, VOCs, and CO2.
* **Source Identification:** A more detailed investigation of the VOC sources would be needed to fully understand the impact of those spikes.
* **Geographic Mapping:**  Mapping the location of these peaks would aid in identifying areas of concern.

---

To give a more tailored analysis, I’d need to know the source of this data.  Is it a real-time monitoring system, or something else?  Knowing the context would allow for a more precise interpretation.

Okay, here's a summary and analysis of the provided data, broken down into key observations:

**1. Overall Trends:**

* **Temperature & Humidity Correlation:** There’s a strong positive correlation between temperature and humidity.  As temperature increases, humidity generally increases. This is a fairly consistent pattern across the dataset.
* **VOC & CO2 Spike:**  There's a noticeable spike in VOC (Volatile Organic Compounds) and CO2 levels, especially around the evening/night hours. This suggests potential events or conditions contributing to these increases.  The magnitude of these spikes varies significantly.
* **Time of Day Variations:**  The data seems to have diurnal variations, with higher values generally occurring during the day (especially after the 12:00 PM mark). There’s a very slight (and perhaps subtle) increase in the evening hours.

**2. Specific Observations & Patterns:**

* **Temperature and Humidity Peaks:**  The data shows periods of high temperatures and humidity, frequently within a few hours of each other.  This suggests a period of elevated environmental conditions.
* **VOC Spike - Timing is Key:** The most significant increase in VOC is concentrated between 12:00 PM and 6:00 PM. This strongly indicates a potential link between increased outdoor activity and increased emissions.
* **Temperature and Humidity Correlation – Consistent Trend:** The temperature and humidity fluctuate around a relatively stable baseline (around 22-28°C), suggesting a thermal equilibrium.

**3. Potential Insights & Questions:**

* **Event Correlation:** Given the spike in VOC and temperature during the day, we could investigate if there’s a link between these conditions and specific events (e.g., increased traffic, construction, outdoor gatherings).
* **Climate Influence:**  The correlations suggest a potential influence of climate patterns on air quality.  Long-term trends in temperature and humidity could be driving these observations.
* **Source of VOC:** Understanding the sources of the VOC is critical.  Are these from industrial emissions, vehicle exhaust, natural sources (vegetation, soil), or something else?
* **Spatial Variation:**  While the data is spread out, it’s important to understand *where* the peaks are occurring.  Are they localized to certain areas or regions?

**4.  Recommendations for Further Analysis**

* **Time-Series Analysis:**  A time-series analysis would help reveal underlying patterns and trends in the data.
* **Correlation Analysis:**  Statistical correlation analysis could be done to understand the strength and significance of the relationships between temperature, humidity, VOCs, and CO2.
* **Source Identification:** A more detailed investigation of the VOC sources would be needed to fully understand the impact of those spikes.
* **Geographic Mapping:**  Mapping the location of these peaks would aid in identifying areas of concern.

---

To give a more tailored analysis, I’d need to know the source of this data.  Is it a real-time monitoring system, or something else?  Knowing the context would allow for a more precise interpretation.

## 1. High‑level summary  

| Period | Avg T (°F) | Min T | Max T | Avg H % | Min H | Max H | Avg PM₂.₅ µg/m³ | Peak PM₂.₅ | Avg VOC index | Avg CO₂ ppm |
|--------|------------|-------|-------|----------|-------|-------|------------------|-------------|---------------|-------------|
| 17 Sep 2025 | 68.4 | 65.8 | 70.4 | 60.7 | 56.4 | 63.9 | 3.9 | 5.1 | 39.4 | 590 |
| 18 Sep 2025 | 69.5 | 65.6 | 70.2 | 59.3 | 56.4 | 65.1 | 3.3 | 4.5 | 38.4 | 466 |

* **Temperature:** 65–70 °F throughout the day, with a gentle rise in the morning (≈65 °F) to a peak in late afternoon (~70 °F).
* **Humidity:** 56–65 % overall, slightly decreasing as the day progresses.
* **PM₂.₅:** Very low (≤ 5 µg/m³) with short‑lived spikes around 4.5–5.1 µg/m³, mainly in the early morning of 17 Sep.
* **VOC index:** 36–47, peaking at 47 (17 Sep, 00:28 h). Mostly stable around 38–40.
* **NOx index:** Constant 1.0 (likely the instrument’s lower detection limit).
* **CO₂:** 400–850 ppm. The highest values (> 800 ppm) occur between 00:00–04:00 on 17 Sep. By 18 Sep, nighttime CO₂ falls to the 530–675 ppm range.

---

## 2. Key patterns & insights  

| Pattern | Observation | Possible explanation |
|---------|-------------|----------------------|
| **Diurnal temperature rise** | 65 °F → 70 °F | Sunlight, heating, indoor thermal inertia |
| **Humidity drop over day** | 65 % → 56 % | Evaporation, increased ventilation, reduced indoor water sources |
| **Morning CO₂ spike (17 Sep)** | 829 ppm at 00:29 → 675 ppm at 00:58 | Possible overnight buildup due to closed windows/poor ventilation; 17 Sep may have had high occupancy or a heat‑pump cycle start |
| **Nighttime CO₂ drop on 18 Sep** | 534 ppm at 00:58 | Better ventilation (windows opened) or lower occupancy |
| **PM₂.₅ spikes at 4.5–5.1 µg/m³** | 17 Sep 00:28, 18 Sep 08:28/12:28 | Likely indoor combustion (cooking, cleaning) or short bursts of outdoor air infiltration |
| **VOC index peak at 47 (17 Sep 00:28)** | 47 | Indoor volatile emissions from cleaning products, new furniture, or HVAC start‑up |
| **Strong correlation between CO₂ & VOC?** | Not obvious from visual scan | CO₂ rises with human occupancy; VOCs can also be occupancy‑related (e.g., cooking). A correlation matrix would clarify |
| **NOx at the detection limit** | Always 1.0 | Indoor environment; outdoor NOx may be negligible or sensor not sensitive enough |

---

## 3. Suggested next steps / further analysis  

1. **Compute daily statistics**  
   * Mean, median, standard deviation, min/max for each sensor per day.  
   * 5‑min rolling averages to smooth noise.

2. **Correlation matrix**  
   * Pearson / Spearman between all variables (T, H, PM₂.₅, VOC, CO₂).  
   * Look for lagged relationships (e.g., CO₂ leading VOC by a few minutes).

3. **Anomaly detection**  
   * Use a simple z‑score or more advanced methods (Isolation Forest) to flag PM₂.₅ and CO₂ spikes.  
   * Identify the exact times and possible triggers (occupancy, HVAC, cooking).

4. **Time‑of‑day profiles**  
   * Group data into hourly bins (00–01, 01–02, …) and compute average values to see typical patterns.  
   * Compare 17 Sep vs 18 Sep to assess day‑to‑day variability.

5. **Occupancy inference**  
   * High CO₂ (> 600 ppm) with low PM₂.₅ may indicate occupied but well‑ventilated spaces.  
   * Combine with temperature/humidity to estimate occupant load.

6. **Ventilation assessment**  
   * Compare CO₂ decay curves after a presumed “ventilation event” (e.g., opening windows).  
   * Estimate air changes per hour (ACH).

7. **Source apportionment**  
   * If VOC data is further detailed (specific compounds), identify likely sources (cleaners, fresh paint, cooking).  
   * Cross‑check with PM₂.₅ to see if both spike simultaneously (combustion source).

8. **Predictive modeling**  
   * Build a simple regression to predict CO₂ based on temperature, humidity, time of day, and occupancy proxy.  
   * Use the model to flag when ventilation is insufficient.

---

## 4. Quick‑look questions for the data owner  

| Question | Why it matters |
|----------|----------------|
| **Did you change HVAC settings or open/close windows on 17 Sep?** | Could explain overnight CO₂ surge. |
| **Was there any cooking, cleaning, or new furniture in the building around 00:28 h?** | Likely source of PM₂.₅ & VOC spike. |
| **Is the CO₂ sensor calibrated?** | The high nighttime values may be sensor drift or poor ventilation. |
| **What is the building occupancy schedule?** | Helps interpret CO₂ and VOC patterns. |

---

### Bottom line  
The environment is generally comfortable: temperatures stay within 65–70 °F, humidity stays moderate, and PM₂.₅ stays below typical indoor limits (< 5 µg/m³). However, there are clear periodic spikes in CO₂ and VOC that suggest ventilation or indoor source issues, especially around midnight on 17 Sep. A deeper statistical analysis (as outlined) will confirm these patterns and help design targeted HVAC or cleaning interventions.

## 1. High‑level summary  

| Period | Avg T (°F) | Min T | Max T | Avg H % | Min H | Max H | Avg PM₂.₅ µg/m³ | Peak PM₂.₅ | Avg VOC index | Avg CO₂ ppm |
|--------|------------|-------|-------|----------|-------|-------|------------------|-------------|---------------|-------------|
| 17 Sep 2025 | 68.4 | 65.8 | 70.4 | 60.7 | 56.4 | 63.9 | 3.9 | 5.1 | 39.4 | 590 |
| 18 Sep 2025 | 69.5 | 65.6 | 70.2 | 59.3 | 56.4 | 65.1 | 3.3 | 4.5 | 38.4 | 466 |

* **Temperature:** 65–70 °F throughout the day, with a gentle rise in the morning (≈65 °F) to a peak in late afternoon (~70 °F).
* **Humidity:** 56–65 % overall, slightly decreasing as the day progresses.
* **PM₂.₅:** Very low (≤ 5 µg/m³) with short‑lived spikes around 4.5–5.1 µg/m³, mainly in the early morning of 17 Sep.
* **VOC index:** 36–47, peaking at 47 (17 Sep, 00:28 h). Mostly stable around 38–40.
* **NOx index:** Constant 1.0 (likely the instrument’s lower detection limit).
* **CO₂:** 400–850 ppm. The highest values (> 800 ppm) occur between 00:00–04:00 on 17 Sep. By 18 Sep, nighttime CO₂ falls to the 530–675 ppm range.

---

## 2. Key patterns & insights  

| Pattern | Observation | Possible explanation |
|---------|-------------|----------------------|
| **Diurnal temperature rise** | 65 °F → 70 °F | Sunlight, heating, indoor thermal inertia |
| **Humidity drop over day** | 65 % → 56 % | Evaporation, increased ventilation, reduced indoor water sources |
| **Morning CO₂ spike (17 Sep)** | 829 ppm at 00:29 → 675 ppm at 00:58 | Possible overnight buildup due to closed windows/poor ventilation; 17 Sep may have had high occupancy or a heat‑pump cycle start |
| **Nighttime CO₂ drop on 18 Sep** | 534 ppm at 00:58 | Better ventilation (windows opened) or lower occupancy |
| **PM₂.₅ spikes at 4.5–5.1 µg/m³** | 17 Sep 00:28, 18 Sep 08:28/12:28 | Likely indoor combustion (cooking, cleaning) or short bursts of outdoor air infiltration |
| **VOC index peak at 47 (17 Sep 00:28)** | 47 | Indoor volatile emissions from cleaning products, new furniture, or HVAC start‑up |
| **Strong correlation between CO₂ & VOC?** | Not obvious from visual scan | CO₂ rises with human occupancy; VOCs can also be occupancy‑related (e.g., cooking). A correlation matrix would clarify |
| **NOx at the detection limit** | Always 1.0 | Indoor environment; outdoor NOx may be negligible or sensor not sensitive enough |

---

## 3. Suggested next steps / further analysis  

1. **Compute daily statistics**  
   * Mean, median, standard deviation, min/max for each sensor per day.  
   * 5‑min rolling averages to smooth noise.

2. **Correlation matrix**  
   * Pearson / Spearman between all variables (T, H, PM₂.₅, VOC, CO₂).  
   * Look for lagged relationships (e.g., CO₂ leading VOC by a few minutes).

3. **Anomaly detection**  
   * Use a simple z‑score or more advanced methods (Isolation Forest) to flag PM₂.₅ and CO₂ spikes.  
   * Identify the exact times and possible triggers (occupancy, HVAC, cooking).

4. **Time‑of‑day profiles**  
   * Group data into hourly bins (00–01, 01–02, …) and compute average values to see typical patterns.  
   * Compare 17 Sep vs 18 Sep to assess day‑to‑day variability.

5. **Occupancy inference**  
   * High CO₂ (> 600 ppm) with low PM₂.₅ may indicate occupied but well‑ventilated spaces.  
   * Combine with temperature/humidity to estimate occupant load.

6. **Ventilation assessment**  
   * Compare CO₂ decay curves after a presumed “ventilation event” (e.g., opening windows).  
   * Estimate air changes per hour (ACH).

7. **Source apportionment**  
   * If VOC data is further detailed (specific compounds), identify likely sources (cleaners, fresh paint, cooking).  
   * Cross‑check with PM₂.₅ to see if both spike simultaneously (combustion source).

8. **Predictive modeling**  
   * Build a simple regression to predict CO₂ based on temperature, humidity, time of day, and occupancy proxy.  
   * Use the model to flag when ventilation is insufficient.

---

## 4. Quick‑look questions for the data owner  

| Question | Why it matters |
|----------|----------------|
| **Did you change HVAC settings or open/close windows on 17 Sep?** | Could explain overnight CO₂ surge. |
| **Was there any cooking, cleaning, or new furniture in the building around 00:28 h?** | Likely source of PM₂.₅ & VOC spike. |
| **Is the CO₂ sensor calibrated?** | The high nighttime values may be sensor drift or poor ventilation. |
| **What is the building occupancy schedule?** | Helps interpret CO₂ and VOC patterns. |

---

### Bottom line  
The environment is generally comfortable: temperatures stay within 65–70 °F, humidity stays moderate, and PM₂.₅ stays below typical indoor limits (< 5 µg/m³). However, there are clear periodic spikes in CO₂ and VOC that suggest ventilation or indoor source issues, especially around midnight on 17 Sep. A deeper statistical analysis (as outlined) will confirm these patterns and help design targeted HVAC or cleaning interventions.

Environmental Sensor Data Summary
This dataset contains 120 readings from environmental sensors collected over approximately 3 days (September 16-18, 2025), with measurements taken roughly every 30 minutes.
Data Overview

Time period: September 16, 12:58 AM through September 18, 6:59 PM (UTC)
Frequency: ~30-minute intervals
Location: Indoor environment (based on temperature stability and CO2 patterns)

Key Patterns and Insights
Temperature Trends

Range: 64.9°F to 70.2°F (relatively narrow 5.3°F span)
Clear daily cycles with cooler temperatures overnight (65-67°F) and warmer during day (68-70°F)
Peak temperatures typically occur in late afternoon/early evening
Gradual warming trend over the 3-day period

Humidity Patterns

Range: 56.4% to 65.4%
Strong inverse correlation with temperature - humidity drops as temperature rises
Daily cycles mirror temperature but inverted
Generally comfortable indoor humidity levels

Air Quality Indicators

PM2.5: Consistently good (2.0-5.1 µg/m³, well below 12 µg/m³ WHO guideline)
VOC Index: Moderate levels (36-47), showing some variability but no concerning spikes
NOx Index: Consistently low (1.0 throughout), indicating minimal nitrogen oxide pollution

CO2 Levels (Most Notable Pattern)

Range: 393-866 ppm with significant daily fluctuations
Daily pattern: Low during day (400-500 ppm), elevated at night (600-800+ ppm)
Peak levels occur around 8-9 PM, suggesting occupancy-driven increases
Some readings above 800 ppm indicate periods of poor ventilation

Key Insights

Indoor Environment: The stable temperature range and CO2 fluctuation patterns strongly suggest this is indoor monitoring of an occupied space
Ventilation Concerns: Evening CO2 spikes above 800 ppm indicate periods when ventilation may be inadequate for the occupancy level
Good Overall Air Quality: PM2.5 and NOx levels are consistently excellent, suggesting good filtration and minimal outdoor pollution infiltration
Normal HVAC Operation: Temperature and humidity patterns suggest a well-functioning climate control system

Suggested Further Analysis

Occupancy Correlation: Cross-reference CO2 peaks with known occupancy schedules to optimize ventilation timing
Energy Efficiency: Analyze the relationship between temperature settings and CO2 levels to balance comfort, air quality, and energy use
Seasonal Monitoring: Continue data collection through different seasons to understand how outdoor conditions affect indoor air quality
Ventilation Optimization: Install CO2-based demand ventilation to automatically increase airflow when levels exceed 600-700 ppm
Long-term Health Tracking: Monitor if elevated evening CO2 correlates with occupant comfort complaints or productivity issues

The data suggests a generally healthy indoor environment with room for improvement in evening ventilation management.

Environmental Sensor Data Summary
This dataset contains 120 readings from environmental sensors collected over approximately 3 days (September 16-18, 2025), with measurements taken roughly every 30 minutes.
Data Overview

Time period: September 16, 12:58 AM through September 18, 6:59 PM (UTC)
Frequency: ~30-minute intervals
Location: Indoor environment (based on temperature stability and CO2 patterns)

Key Patterns and Insights
Temperature Trends

Range: 64.9°F to 70.2°F (relatively narrow 5.3°F span)
Clear daily cycles with cooler temperatures overnight (65-67°F) and warmer during day (68-70°F)
Peak temperatures typically occur in late afternoon/early evening
Gradual warming trend over the 3-day period

Humidity Patterns

Range: 56.4% to 65.4%
Strong inverse correlation with temperature - humidity drops as temperature rises
Daily cycles mirror temperature but inverted
Generally comfortable indoor humidity levels

Air Quality Indicators

PM2.5: Consistently good (2.0-5.1 µg/m³, well below 12 µg/m³ WHO guideline)
VOC Index: Moderate levels (36-47), showing some variability but no concerning spikes
NOx Index: Consistently low (1.0 throughout), indicating minimal nitrogen oxide pollution

CO2 Levels (Most Notable Pattern)

Range: 393-866 ppm with significant daily fluctuations
Daily pattern: Low during day (400-500 ppm), elevated at night (600-800+ ppm)
Peak levels occur around 8-9 PM, suggesting occupancy-driven increases
Some readings above 800 ppm indicate periods of poor ventilation

Key Insights

Indoor Environment: The stable temperature range and CO2 fluctuation patterns strongly suggest this is indoor monitoring of an occupied space
Ventilation Concerns: Evening CO2 spikes above 800 ppm indicate periods when ventilation may be inadequate for the occupancy level
Good Overall Air Quality: PM2.5 and NOx levels are consistently excellent, suggesting good filtration and minimal outdoor pollution infiltration
Normal HVAC Operation: Temperature and humidity patterns suggest a well-functioning climate control system

Suggested Further Analysis

Occupancy Correlation: Cross-reference CO2 peaks with known occupancy schedules to optimize ventilation timing
Energy Efficiency: Analyze the relationship between temperature settings and CO2 levels to balance comfort, air quality, and energy use
Seasonal Monitoring: Continue data collection through different seasons to understand how outdoor conditions affect indoor air quality
Ventilation Optimization: Install CO2-based demand ventilation to automatically increase airflow when levels exceed 600-700 ppm
Long-term Health Tracking: Monitor if elevated evening CO2 correlates with occupant comfort complaints or productivity issues

The data suggests a generally healthy indoor environment with room for improvement in evening ventilation management.