Health impact assessment of exposure to fine particulate matter based on satellite and meteorological information

Assessment and valuation of health impacts of fine particulate matter during COVID-19 lockdown: a comprehensive study of tropical and sub tropical countries

A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.

Estimation of health benefits from air quality improvement using the MODIS AOD dataset in Seoul, Korea

BACKGROUND

Exposure to fine particles in the atmosphere can adversely affect health and even lead to premature death. Recently, South Korea has attracted attention because of its rapid increase in the concentration of Particulate Matter (PM).

OBJECTIVES

We estimated the economic benefits of reducing PM₁₀ in Seoul, South Korea, based on MODerate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD). Based on the retrieved PM₁₀ data, we estimated its effects on overall health in each district of Seoul, Korea between 2014 and 2015.

METHODS

The relationships between MODIS AOD and ground-based PM₁₀ data were identified in different seasons in South Korea between 2012 and 2013 using the linear regression model. The health benefits were estimated by the Benefits Mapping and Analysis Program (Benmap) using the scenarios from the World Health Organization (WHO).

RESULTS

The correlation between MODIS AOD and PM₁₀ concentration differed with the season. There was a higher correlation between MODIS AOD and PM₁₀ concentration in winter (R = 0.57) than there was in other seasons. Based on the MODIS AOD, the average annual PM₁₀ concentration in Seoul was higher in 2014 than it was in 2015, at values of 45.7 μg/m³, and 41.6 μg/m³, respectively. The greatest economic benefit of reducing PM₁₀ concentration (WHO annual standard of 20 μg/m³) was in 2014. This benefit was estimated to be 7022 (95% CI: 599, 20496), 2617 (95% CI: 216, 7750), and 1328 (95% CI: -159, 4679) billion KRW for all-cause, cardiovascular, and respiratory mortalities in 2014 and 2015, respectively.

CONCLUSIONS

These results demonstrate that, despite considerable improvements in air quality in recent decades, there is still a need for countermeasures to prevent economic loss due to air pollution in Seoul.

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5

Fine particulate matter (PM_2.5) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM_2.5, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM_2.5, especially in areas with high spatiotemporal variability of PM_2.5. In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation. In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Cancer Mortality Risks from Long-term Exposure to Ambient Fine Particle

BACKGROUND

Few studies have assessed long-term effects of particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5) on mortality for causes of cancer other than the lung; we assessed the effects on multiple causes. In Hong Kong, most people live and work in urban or suburban areas with high-rise buildings. This facilitates the estimation of PM2.5 exposure of individuals, taking into account the height of residence above ground level for assessment of the long-term health effects with sufficient statistical power.

METHODS

We recruited 66,820 persons who were ≥65 in 1998 to 2001 and followed up for mortality outcomes until 2011. Annual concentrations of PM at their residential addresses were estimated using PM2.5 concentrations measured at fixed-site monitors, horizontal-vertical locations, and satellite data. We used Cox regression model to assess the HR of mortality for cancer per 10 μg/m(3) increase of PM2.5 RESULTS: PM2.5 was associated with increased risk of mortality for all causes of cancer [HR, 1.22 (95% CI, 1.11-1.34)] and for specific cause of cancer in upper digestive tract [1.42 (1.06-1.89)], digestive accessory organs [1.35 (1.06-1.71)] in all subjects; breast [1.80 (1.26-2.55)] in females; and lung [1.36 (1.05-1.77)] in males.

CONCLUSIONS

Long-term exposures to PM2.5 are associated with elevated risks of cancer in various organs.

IMPACT

This study is particularly timely in China, where compelling evidence is needed to support the pollution control policy to ameliorate the health damages associated with economic growth. Cancer Epidemiol Biomarkers Prev; 25(5); 839-45. ©2016 AACR.