Exposure and dose assessment of school children to air pollutants in a tropical coastal-urban area

Refining Children's exposure assessment to NO2, SO2, and O3: Incorporating indoor-to-outdoor concentration ratios and individual daily routine

Exposure to air pollutants like sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3) is associated with adverse health effects, particularly with exacerbations of asthma symptoms and new asthma cases in both children and adults. While fixed-site monitoring (FSM) stations are commonly used in air pollutant exposure studies, they may not fully capture personal exposures due to limitations such as inadequate consideration of daily routines and indoor/outdoor concentration variations. In this study, to enhance the accuracy of personal exposure calculated by using FSM data, individual's daily activity routine, encompassing both indoor and outdoor environments, were incorporated by using indoor-to-outdoor concentration ratios. Three methodologies were compared to assess the accuracy of exposure calculations: (i) direct exposure determination employing passive samplers (PS), (ii) personal exposure calculated using FSM data alone, and (iii) personal exposure calculated using FSM data refined by integrating local average individual daily activity routines and indoor-to-outdoor ratios. The results demonstrate that the refined method (iii) yields substantial improvements in estimated exposure levels, reducing the average error from 1.4% to 0.4% for NO2, from 72.1% to 12.7% for SO2, and from 323.4% to 24.9% for O3.

Exploring the association between long-term MODIS aerosol and air pollutants data across the Northern Great Plains through machine learning analysis

Aerosol optical depth (AOD) and Ångström exponent (AE) are the major environmental indicators to perceive air quality and the impact of aerosol on climate change and health as well as the global atmospheric conditions. In the present study, an average of AOD and AE data from Tera and Aqua satellites of MODIS sensors has been investigated over 7 years i.e., from 2016 to 2022, at four locations over Northern Great Plains. Both temporal and seasonal variations over the study periods have been investigated to understand the behavior of AOD and AE. Over the years, the highest AOD and AE were observed in winter season, varying from 0.75 to 1.17 and 1.30 to 1.63, respectively. During pre-monsoon season, increasing trend of AOD varying from 0.65 to 0.95 was observed from upper (New Delhi) to lower (Kolkata) Gangetic plain, however, during monsoon and post-monsoon a reverse trend varying from 0.85 to 0.65 has been observed. Seasonal and temporal aerosol characteristics have also been analyzed and it has been assessed that biomass burning was found to be the major contributor, followed by desert dust at all the locations except in Lucknow, where the second largest contributor was dust instead of desert dust. During season-wise analysis, biomass burning was also found to be as the major contributor at all the places in all the seasons except New Delhi and Lucknow, where dust was the major contributor during pre-monsoon. A boosting regression algorithm was done using machine learning to explore the relative influence of different atmospheric parameters and pollutants with PM2.5. Water vapor was assessed to have the maximum relative influence i.e., 51.66 % followed by CO (21.81 %). This study aims to help policy makers and decision makers better understand the correlation between different atmospheric components and pollutants and the contribution of different types of aerosols.

Multi-scenario PM2.5 distribution and dynamic exposure assessment of university community residents: Development and application of intelligent health risk management system integrated low-cost sensors

Exposure scenario and receptor behavior significantly affect PM2.5 exposure quantity of persons and resident groups, which in turn influenced indoor or outdoor air quality & health management. An Internet of Things (IoT) system, EnvironMax+, was developed to accurately and conveniently assess residential dynamic PM2.5 exposure state. A university community "QC", as the application area, was divided into four exposure scenarios and five groups of residents. Low-cost mobile sensors and indoor/outdoor pollution migration (IOP) models jointly estimated multi-scenario real-time PM2.5 concentrations. Questionnaire was used to investigate residents' indoor activity characteristics. Mobile application (app) "Air health management (AHM)" could automatic collect residents' activity trajectory. At last, multi-scenario daily exposure concentrations of each residents-group were obtained. The results showed that residential exposure scenario was the most important one, where residents spend about 60 % of their daily time. Closing window was the most significant behavior affecting indoor contamination. The annual average PM2.5 concentration in the studied scenarios: residential scenario (RS) < public scenario (PS) < outdoor scenario (OS) < catering scenario (CS). Except for CS, the outdoor PM2.5 in other scenarios was higher than indoor by 5-10 μg/m3. The multi-scenario population weighted annual average exposure concentration was 37.1 μg/m3, which was 78 % of the annual average outdoor concentration. The exposure concentration of 5 groups: cooks > outdoor workers > indoor workers > students > the elderly, related to their daily activity time proportion in different exposure scenario.