Associations between Acute Exposures to PM2.5 and Carbon Dioxide Indoors and Cognitive Function in Office Workers: A Multicountry Longitudinal Prospective Observational Study.

Despite evidence of the air pollution effects on cognitive function, little is known about the acute impact of indoor air pollution on cognitive function among the working-age population. We aimed to understand whether cognitive function was associated with real-time indoor concentrations of particulate matter (PM2.5) and carbon dioxide (CO2). We conducted a prospective observational longitudinal study among 302 office workers in urban commercial buildings located in six countries (China, India, Mexico, Thailand, the United States of America, and the United Kingdom). For 12 months, assessed cognitive function using the Stroop color-word test and Addition-Subtraction test (ADD) via a mobile research app. We found that higher PM2.5 and lower ventilation rates, as assessed by CO2 concentration, were associated with slower response times and reduced accuracy (fewer correct responses per minute) on the Stroop and ADD for 8 out 10 test metrics. Each interquartile (IQR) increase in PM2.5 (IQR=8.8 µg/m3) was associated with a 0.82% (95%CI: 0.42, 1.21) increase in Stroop response time, a 6.18% (95% CI: 2.08, 10.3) increase in Stroop interference time, a 0.7% (95% CI: -1.38, -0.01) decrease in Stroop throughput, and a 1.51% (95% CI: -2.65, -0.37) decrease in ADD throughput. For CO2, an IQR increase (IQR=315ppm) was associated with a 0.85% (95% CI: 0.32, 1.39) increase in Stroop response time, a 7.88% (95% CI: 2.08, 13.86) increase in Stroop interference time, a 1.32% (95% CI: -2.3, -0.38) decrease in Stroop throughput, and a 1.13% (95% CI: 0.18, 2.11) increase in ADD response time. A sensitivity analysis showed significant association between PM2.5 in four out of five cognitive test performance metrics only at levels above 12 µg/m3. Enhanced filtration and higher ventilation rates that exceed current minimum targets are essential public health strategies that may improve employee productivity.

Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors.

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM2.5 data in Taiwan. We developed models for estimating ground-level PM2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM2.5 data from the Airbox sensors (AB-PM2.5) was used to predict daily mean PM2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM2.5 at all the grids in the previous year. The modeling-predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R2 increased from 0.40 to 0.87 when the AB-PM2.5 data were included as a nonlinear component in the model, indicating that AB-PM2.5 is a significant predictor of ground-level PM2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM2.5 concentrations were smaller than 5 µg/m3 and the R2 of the CV models of 0.79-0.88 during 2006-2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM2.5 for cohorts of small areas in health impact assessment studies.

Short-Term PM2.5 Forecasting Using Exponential Smoothing Method: A Comparative Analysis.

Air pollution is a global problem and can be perceived as a modern-day curse. One way of dealing with it is by finding economical ways to monitor and forecast air quality. Accurately monitoring and forecasting fine particulate matter (PM2.5) concentrations is a challenging prediction task but Internet of Things (IoT) can help in developing economical and agile ways to design such systems. In this paper, we use a historical data-based approach to perform PM2.5 forecasting. A forecasting method is developed which uses exponential smoothing with drift. Experiments and evaluation were performed using the real-time PM2.5 data obtained from large scale deployment of IoT devices in Taichung region in Taiwan. We used the data from 132 monitoring stations to evaluate our model's performance. A comparison of prediction accuracy and computation time between the proposed model and three widely used forecasting models was done. The results suggest that our method can perform PM2.5 forecast for 132 monitoring stations with error as low as 0.16 µ g/ m 3 and also with an acceptable computation time of 30 s. Further evaluation was done by forecasting PM2.5 for next 3 h. The results show that 90 % of the monitoring stations have error under 1.5 µ g/ m 3 which is significantly low.