Developing an Advanced PM2.5 Exposure Model in Lima, Peru

Vu, Bryan N.; Sànchez, Odòn; Bi, Jianzhao; Xiao, Qingyang; Hansel, Nadia N.; Checkley, William; Gonzáles, Gustavo F.; Steenland, Kyle; Liu, Yang

doi:10.3390/rs11060641

Cited by 47 publications

(50 citation statements)

References 42 publications

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“…We assessed the data of COVID-19 cases and deaths that occurred in Lima metropolitan area until June 12, 2020. We comparatively analyzed the data on COVID-19 with the estimate daily levels of PM 2.5 measured in the years between 2012 and 2016 [17],…”

Section: Methodsmentioning

confidence: 99%

Association Between Air Pollution in Lima and the High Incidence of COVID-19: Findings from a Post Hoc Analysis

Vasquez-Apestegui

Parras-Garrido

Tapia

et al. 2020

Preprint

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Abstract Background Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America and it is also one of the cities with highest levels of air pollution in the Region. Methods This study investigated the association of the levels of PM2.5 exposure in the previous years (2010–2016) in 24 districts of Lima with the cases, deaths and case-fatality rates of COVID-19. Results Until June 12, 2020, there were 6,308 deaths and 220,749 SARS-CoV-2 positive cases in Peru. In Lima, the total number of COVID-19 deaths in all metropolitan areas was 2,382. The case-fatality rate at the national level was 2.58% and 1.93% in Lima. Higher PM2.5 levels are associated with higher number of cases and deaths of COVID-19. The case-fatality rate (Deaths/cases*100) did not increase with the increase in PM2.5 levels. A higher number of food markets was associated with higher incidence and mortality of COVID-19 (p < 0.01 for both); these associations persisted when cases (r = 0.49; p < 0.01) and deaths (r = 0.58; p < 0.01) were adjusted by the population density. The association of PM2.5 with cases of COVID-19 was maintained after controlling analysis by age, sex and number of food markers. Conclusions the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long term perspective, and social distancing are needed to prevent spreads of virus outbreak.

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Section: Methodsmentioning

confidence: 99%

Association Between Air Pollution in Lima and the High Incidence of COVID-19: Findings from a Post Hoc Analysis

Vasquez-Apestegui

Parras-Garrido

Tapia

et al. 2020

Preprint

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“…In this study, we estimated PM 2.5 data using a satellite-driven PM 2.5 exposure model [20] which provided daily population-weighted average PM 2.5 concentrations for all districts of Lima, from 2010 to 2016. We then examined PM 2.5 short-term exposure in relation to all cause and cause-specific mortality in Lima, one of the most polluted cities in Latin America [12].…”

Section: Discussionmentioning

confidence: 99%

“…Hence, the ground-monitoring network was considered too sparse to adequately capture the spatiotemporal variability in PM 2.5 levels that occurs in Lima. Thus, we based our PM 2.5 exposure data from a model developed by Vu et al [20]. Briefly, daily PM 2.5 concentrations at a 1 km 2 spatial resolution for 2010-2016 were estimated using a combination of the available ground measurements plus aerosol optical depth (AOD) data from satellites, and meteorological and land use data chemical transport models.…”

Section: Meteorological and Ambient Pm 25 Datamentioning

confidence: 99%

PM2.5 exposure on daily cardio-respiratory mortality in Lima, Peru, from 2010 to 2016.

Tapia

Steenland

et al. 2020

Preprint

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Background: There have been no studies of air pollution and mortality in Lima, Peru. We evaluate whether daily environmental PM 2.5 exposure is associated to respiratory and cardiovascular mortality in Lima during 2010 to 2016. Methods: We analyzed 86,970 deaths from respiratory and cardiovascular diseases in Lima from 2010-2016. Estimated daily PM 2.5 was assigned based on district of residence. Poisson regression was used to estimate associations between daily district-level PM 2.5 exposures and daily counts of deaths. Results: An increase in 10 µg/m 3 PM 2.5 on the day before was significantly associated with daily all-cause (respiratory and circulatory) mortality (RR 1.029; CI 95% CI: 1.01 – 1.05) across all ages and in the age group over 65 (RR 1.04; 95% CI: 1.005 – 1.09) which included 74% of all deaths. We also observed associations with circulatory deaths for all age groups (RR 1.06; 95% CI: 1.01 – 1.11), and those over 65 (RR 1.06; 95% CI 1.00 - 1.12). A borderline significant trend was seen (RR 1.05; 95% CI 0.99 – 1.06; p = 0.10) for respiratory deaths in persons aged over 65. Trends were driven by the highest quintile of exposure. Conclusions: PM 2.5 exposure is associated with daily all-cause, cardiovascular and respiratory mortality in Lima, especially for older people. Our data suggest that the existing limits on air pollution exposure are too high.

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“…Previous studies show that some outliers negatively affect the accuracy and robustness of PM 2.5 retrieval modelling [49][50][51]58]. Therefore, in our study, we excluded PM 2.5 and AOD data in three conditions: (1) the AOD < 2.5; (2) the AOD > 0.5 and PM 2.5 < 10 µg/m 3 ; and (3) the PM 2.5 < 3 µg/m 3 .…”

Section: Data Pre-processing and Integrationmentioning

confidence: 99%

“…Hu et al [48] adopted the fixed rank smoothing to fill the data gaps in 3 km AOD data, and proposed a spatiotemporal regression kriging (STRK) model to obtain accurate daily PM 2.5 estimations with full-coverage. Zhao [49] and Vu [50] used the random forest model to estimate the corresponding full-coverage AOD data. Xue et al [51] developed a machine learning model with high dimensional expansion of numerous predictors and incorporated a generalized additive model into the model to obtain a full-coverage PM 2.5 estimation.…”

Section: Introductionmentioning

confidence: 99%

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

et al. 2019

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Current PM2.5 retrieval maps have many missing values, which seriously hinders their performance in real applications. This paper presents a framework to map full-coverage daily average PM2.5 concentrations from MODIS C6 aerosol optical depth (AOD) products and fill missing pixels in both the AOD and PM2.5 maps. First, a two-stage inversed variance weights (IVW) algorithm was adopted to fuse the MODIS C6 Terra and Aqua AOD products, which fills missing data in MODIS standard AOD data and obtains a high coverage daily average. After that, using the fused MODIS daily average AOD and ground-level PM2.5 in all grid cells, a two-stage generalized additive model (GAM) was implemented to obtain the full-coverage PM2.5 concentrations. Experiments on the Yangtze River Delta (YRD) in 2013–2016 were carefully designed to validate the performance of our proposed framework. The results show that the two-stage IVW could not only improve the spatial coverage of MODIS AOD against the original standard product by 230%, but could also keep its data accuracy. When compared with the ground-level measurements, the two-stage GAM can obtain accurate PM2.5 concentration estimates (R2 = 0.78, RMSE = 19.177 μg/m3, and RPE = 28.9%). Moreover, our method performs better than the inverse distance weighted method and kriging methods in mapping full-coverage daily PM2.5 concentrations. Therefore, the proposed framework provides a good methodology for retrieving full-coverage daily average PM2.5 concentrations from MODIS standard AOD products.

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Developing an Advanced PM2.5 Exposure Model in Lima, Peru

Cited by 47 publications

References 42 publications

Association Between Air Pollution in Lima and the High Incidence of COVID-19: Findings from a Post Hoc Analysis

Association Between Air Pollution in Lima and the High Incidence of COVID-19: Findings from a Post Hoc Analysis

PM2.5 exposure on daily cardio-respiratory mortality in Lima, Peru, from 2010 to 2016.

A Full-Coverage Daily Average PM2.5 Retrieval Method with Two-Stage IVW Fused MODIS C6 AOD and Two-Stage GAM Model

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