Design and application of a web-based real-time personal PM2.5 exposure monitoring system

Calibration of a low-cost PM2.5 monitor using a random forest model

BACKGROUND

Particle air pollution has adverse health effects, and low-cost monitoring among a large population group is an effective method for performing environmental health studies. However, concern about the accuracy of low-cost monitors has affected their popularization in monitoring projects.

OBJECTIVE

To calibrate a low-cost particle monitor (HK-B3, Hike, China) through a controlled exposure experiment.

METHODS

Our study used a MicroPEM monitor (RTI, America) as a standard particle concentration measurement device to calibrate the Hike monitors. A machine learning model was established to calibrate the particle concentration obtained by the low-cost PM_2.5 monitors, and ten-fold validation was used to test the model. In addition, we used a linear regression model to compare the results of the machine learning model. A calibration method was established for the low-cost monitors, and it can be used to apply the monitors in future air pollution monitoring projects.

RESULTS

The values of the random forest model calibration results and observations were more condensed around the regression line y = 0.99x + 0.05, and the R squared value (R² = 0.98) was higher than that for the linear regression (R² = 0.87). The random forest model showed better performance than the traditional linear regression model.

CONCLUSIONS

Our study provided an effective calibration method to support the accuracy of low-cost monitors. The machine learning method based on the calibration model established in our study can increase the effectiveness of future air pollution and health studies.

Environmental Health Indicators for China: Data Resources for Chinese Environmental Public Health Tracking

Many developed countries use environmental public health tracking to gain a better understanding of the link between environmental hazards and public health. To respond to complicated environmental health issues, the National Institute of Environmental Health (NIEH), Chinese Center for Disease Control and Prevention (China CDC), has begun to build a Chinese Environmental Public Health Tracking (CEPHT) system. On behalf of the CEPHT, authors provide insight into the CEPHT's development, current status, and future plans. In the initial stage of CEPHT, an indicator framework linking environment and public health that included a list of publicly available data sources regarding environmental hazards, public health outcomes, and risk factors in China was developed. An analysis of data availability, along with a comparison between CEPHT's indicator system and other tracking networks, revealed the existence of barriers and gaps in data integration that affect China's ability to track environmental public health. The lack of access to data, combined with inadequate data quality, has led to difficulties linking environmental hazards to their effects on public health. Current CEPHT efforts will help integrate environmental factors and exposure data with public health outcomes. For the near future, CEPHT plans to focus on increasing collaboration among data tracking agencies, improving data quality, and expanding proper data sharing. https://doi.org/10.1289/EHP4319.