Algorithm 1. for air pollution spikes detection in transportation for better health

Input: Sensor data (Particulate matter levels, Temperature, Humidity, O3, NO2, SO2, CO2)  Output: Pollution level, alerts for spikes or dangerous levels Step 1: Data Collection Set up air quality monitoring sensors at predetermined locations. Continuously collect sensor data, including readings for various pollutants such as Temperature, Humidity, PM2.5, PM10, CO2, NO2, O3, and SO2. Record the timestamp for each data point. Step 2: Data Preprocessing Check for sensor anomalies and calibration issues. Handle missing or erroneous data through interpolation or data imputation techniques. Smooth the data to reduce noise using filters or moving averages. Step 3: Spike Detection Define a threshold for each pollutant. Compare the current data by applying the Heaviside step, Direc delta, Sigmoid, and Spike functions with the threshold values. If any parameter exceeds the threshold, generate an alert indicating a pollution spike. Step 4: Data Reporting and Visualization Display real-time or periodic pollution data on a dashboard. Provide historical pollution data for analysis and comparison. Step 5: Mitigation and Response Implement strategies to reduce pollution sources if persistent high pollution levels are detected. Step 6: Data Storage Store collected data in a secure and accessible database for future analysis and research. Step 7: Continuous Monitoring Continuously run the algorithm, repeating steps 1 to 5, to ensure ongoing air quality monitoring.