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

An exploratory analysis of PM 2.5 /PM 10 ratio during spring 2016-2018 in Metropolitan Lima

Aerosols (PM2.5 and PM10 ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM2.5 /PM10 ratios with climatic variables in the austral spring (2016-2018) in Metropolitan Lima. Overall, there was an average PM2.5 /PM10 ratio of 0.33 with fluctuations from 0.30 to 0.35. However, there have also been high point values that reached ratios greater than one. This situation indicates a moderate condition of contamination by particulate matter with a predominance of coarse aerosols in spring, with an increasing trend over the years. The locations Ate and Villa Maria del Triunfo, especially Ate, presented poor quality conditions. Thursdays showed outstanding pollution peaks by PM10 , and a decrease is visible on Sundays. On the other hand, the PM2.5 showed a similar pattern every day, including Sundays. The maximum peaks occurred in the morning and night hours. The increase in anthropogenic emissions associated with the formation of secondary aerosols has been evident, being the case of the location Campo de Marte, the one that had a significant increase in ratios PM2.5 /PM10 , which confirms a greater intensity of secondary formations of carbonaceous particles from industrial oil sources, vehicle exhaust, as well as aerosols from metal smelting and biomass burning. There were negative correlations of the ratios with PM10 , temperature, wind speed, and direction, and positive correlations with PM2.5 and relative humidity. Contour lines were successfully developed that demonstrated the interaction of climate with PM2.5 /PM10 ratios. This will deepen the exploration of emission sources and modeling, which allows for optimizing air quality indices to control emissions and adequately manage air quality in Metropolitan Lima.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5 years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2 µg/m3) and southern Lima (VMT station-126.7 µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8 µg/m3 and 34.0 µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.

Optimizing modeling windows to better capture the long-term variation of PM2.5 concentrations in China during 2005-2019

Including data of different time intervals during model development influences the predicting accuracy of PM2.5 but has not been widely discussed. Therefore, we included modeling data with multiple time windows to identify optimized modeling time windows for capturing the long-term variation of PM2.5 in China during 2005-2019. In general, we incorporated PM2.5 measurements, aerosol optical depth (AOD), meteorological parameters, land use data, and other predictors to train random forest models. The study period was separated into two phases (2013-2019 and 2005-2012) according to the availability of PM2.5 measurements. First, we trained models with two strategies of choosing time windows to compare model performance in predicting PM2.5 from 2013 to 2019, when measurements were available. Strategy 1a (ST1a) refers to training one model with all available data, and Strategy 1b (ST1b) refers to training multiple models each with one-year data. Second, we trained models with additional ten strategies (ST2a-ST2j) based on data from different time windows during 2013-2019 to compare the accuracy in predicting PM2.5 before 2013, when measurements were unavailable. The internal and external cross-validation (CV) indicated that the model performance of ST1b was better than ST1a. Predictions based on ST1a tended to underestimate PM2.5 levels in 2013 and 2014 when PM2.5 concentrations were high, and overestimate after 2017 when PM2.5 dropped dramatically. The external CV of predicting historical PM2.5 was the most robust in ST2i (averaged predictions from two models developed by 2013 and 2014 data, respectively). Models with data closer to historical years and PM2.5 levels performed better in predicting historical PM2.5 concentrations. Our results suggested that training models with data of current-years performed better during 2013-2019, and with data of 2013 and 2014 performed better in predicting historical PM2.5 before 2013 in China. The comparison provided evidence for choosing optimized time windows when predicting long-term PM2.5 concentrations in China.

Intrauterine and Extrauterine Environmental PM2.5 Exposure Is Associated with Overweight/Obesity (O/O) in Children Aged 6 to 59 Months from Lima, Peru: A Case-Control Study

There is evidence that PM2.5 could be obesogenic. Lima is one of the most polluted cities in South America, with an increasing prevalence of childhood obesity. This study aimed to determine the association between PM2.5 exposure of children aged 6 to 59 months and being overweight or obese (O/O) in a significant dataset survey. Cases were defined when weight for height Z-score (WHZ) was >2 standard deviations (SD) from the mean, for each sex. A control was defined when WHZ was between ±2 SD. We used a conditional logistic regression model to calculate the odds ratio (OR) between extrauterine and intrauterine PM2.5 exposure and O/O. Extrauterine PM2.5 exposure was evaluated as a 6-month PM2.5 mean prior to the survey. We found a significant association between O/O and extrauterine (OR: 1.57, 1.51−1.63) and intrauterine (OR: 1.99, 1.88−2.12) PM2.5 exposure for an increment of 10 μg/m3. The ORs increased as the quartile increased in both exposures. We observed a higher association in children aged 6−11 months (OR: 3.07, 2.84−3.31). In conclusion, higher levels of PM2.5 in Lima and Callao were associated with cases of O/O in children from 6 to 59 months, with the association higher for prenatal exposure.

Effect modification by maximum temperature of the association between PM2.5 and short-term cardiorespiratory mortality and emergency room visits in Lima, Peru, 2010-2016

BACKGROUND

The health effects of fine particulate matter (PM_2.5) may be worse at higher temperatures.

OBJECTIVE

To investigate temperature's effect on PM_2.5-mortality/morbidity associations in Lima, Peru.

METHODS

Time-series regressions relating PM_2.5 and temperature to mortality and emergency room (ER) visits during 2010-2016. Daily PM_2.5 levels (assigned to 40 Lima districts) and daily maximum temperature (Lima-wide) were estimated based on ground monitors, remote sensing, and modeling. We analyzed all-cause, cardiovascular (ICD codes I00-I99), and respiratory (ICD codes J00-J99) mortality, and cardiovascular and respiratory causes for ER visits.

RESULTS

The average PM_2.5 concentration was 20.9 µg/m³ (IQR 17.5-23.5). The mean daily maximum temperature was 23.8 °C (IQR 20.8-26.9). PM_2.5's effect on all-cause, respiratory, and circulatory disease mortality was significantly (p < 0.05) stronger at temperatures above the maximum temperature median. The rate ratios per increase of 10 µg/m³ of PM_2.5 for all cause, respiratory, and circulatory mortality respectively were 1.03 (1.00-1.06), 1.04 (0.98-1.10), and 1.04 (0.98-1.10) at temperatures below the median, vs. 1.08 (1.04-1.12), 1.11 (1.03-1.19), and 1.14 (1.05-1.25) when temperatures were above the median. Results were analogous for ER visits for respiratory but not circulatory disease.

SIGNIFICANCE

Results strengthen the evidence that air pollution may be more dangerous when temperatures are higher.

IMPACT

Our data contribute to a growing body of literature which indicates that the damaging effects of PM_2.5 may be worse at higher temperature, adding new evidence from Lima, Peru.

New Homogeneous Spatial Areas Identified Using Case-Crossover Spatial Lag Grid Differences between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Hospitalizations

Optimal use of Hierarchical Bayesian Model (HBM)-assembled aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM2.5 fused surfaces and four respiratory–cardiovascular hospital events in 12 km2 grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included five grids (lag grids 04; 720 km2) and the smallest HOSA contained two grids (lag grids 01; 288 km2). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM2.5 concentration levels, population density, and poverty percentages than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory–cardiovascular chronic diseases in persons residing in rural areas was discussed.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Child Survival and Early Lifetime Exposures to Ambient Fine Particulate Matter in India: A Retrospective Cohort Study

BACKGROUND

Ambient fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] is a major health risk for children, particularly in South Asia, which currently experiences the highest PM2.5 levels globally. Nevertheless, there is comparatively little epidemiological evidence from this region to quantify the effects of PM2.5 on child survival.

OBJECTIVES

We estimated the association between PM2.5 exposure and child survival in India.

METHODS

We constructed a large, retrospective, and nationally representative cohort of children <5 years of age, born between 2009-2016, from the publicly available, cross-sectional 2015-2016 Demographic Health Surveys in India. In utero and post-delivery lifetime average ambient PM2.5 exposures were estimated with data from satellite remote sensing, meteorology, and land use information (model R2= 0.82). We used Cox proportional hazards regression to estimate the association between both average in utero and post-delivery lifetime PM2.5 and all-cause child mortality, controlling for individual- and household-level covariates, seasonality, location, and meteorology.

RESULTS

Over 7,447,724 child-months of follow-up, there were 11,559 deaths at <5 years of age reported by the children's mothers. The mean concentrations of 9-month in utero and post-delivery lifetime average ambient PM2.5 exposure were 71.1 μg/m3 (range: 20.9-153.5 μg/m3) and 73.7 μg/m3 (range: 14.0-247.3 μg/m3), respectively. Estimated child mortality adjusted hazard ratios were 1.023 [95% confidence interval (CI): 1.008, 1.038] and 1.013 (95% CI: 1.001, 1.026) per 10-μg/m3 increase of in utero and post-delivery lifetime PM2.5, with both exposures in the model.

DISCUSSION

This study adds to the growing body of evidence about the adverse health effects of PM2.5 by demonstrating the association between exposure, both in utero and post-delivery, on child survival at the national level in India. Strategies to reduce ambient air pollution levels, including steps to minimize in utero and early life exposures, are urgently needed in India and other countries where exposures are above recommended guideline values. https://doi.org/10.1289/EHP8910.

Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach

Fine particulate matter (PM_2.5) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM_2.5 measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM_2.5 estimates in a study area. In this study, we used AOD estimates of surface PM_2.5 together with meteorological and land use variables to estimate monthly PM_2.5 concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m³. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m³, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m³, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM_2.5 estimates over the entire island, which has complex terrain in terms of land use and topography.

Association between maximum temperature and PM2.5 with pregnancy outcomes in Lima, Peru

BACKGROUND

We have previously documented an inverse relationship between PM2.5 in Lima, Peru, and reproductive outcomes. Here, we investigate the effect of temperature on birth weight, birth weight-Z-score adjusted for gestational age, low birth weight, and preterm birth. We also explore interactions between PM2.5 and temperature.

METHODS

We studied 123,034 singleton births in three public hospitals of Lima with temperature and PM2.5 during gestation between 2012 and 2016. We used linear, logistic, and Cox regression to estimate associations between temperature during gestation and birth outcomes and explored possible modification of the temperature effect by PM2.5.

RESULTS

Exposure to maximum temperature in the last trimester was inversely associated with both birth weight [β: -23.7; 95% confidence interval [CI]: -28.0, -19.5] and z-score weight-for-gestational-age (β: -0.024; 95% CI: -0.029, -0.020) with an interquartile range of 5.32 °C. There was also an increased risk of preterm birth with higher temperature (interquartile range) in the first trimester (hazard ratio: 1.04; 95% CI: 1.001, 1.070). The effect of temperature on birthweight was primarily seen at higher PM2.5 levels. There were no statistically significant associations between temperature exposure with low birth weight.

CONCLUSIONS

Exposition to maximum temperature was associated with lower birth weight and z-score weight-for-gestational-age and higher risk of preterm birth, in accordance with much of the literature. The effects on birth weight were seen only in the third trimester.

The effect of national protest in Ecuador on PM pollution

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

The association between asthma emergency department visits and satellite-derived PM2.5 in Lima, Peru

BACKGROUND

Asthma affects millions of people worldwide. Lima, Peru is one of the most polluted cities in the Americas but has insufficient ground PM2.5 (particulate matter that are 2.5 μm or less in diameter) measurements to conduct epidemiologic studies regarding air pollution. PM2.5 estimates from a satellite-driven model have recently been made, enabling a study between asthma and PM2.5.

OBJECTIVE

We conducted a daily time-series analysis to determine the association between asthma emergency department (ED) visits and estimated ambient PM2.5 levels in Lima, Peru from 2010 to 2016.

METHODS

We used Poisson generalized linear models to regress aggregated counts of asthma on district-level population weighted PM2.5. Indicator variables for hospitals, districts, and day of week were included to account for spatial and temporal autocorrelation while assessing same day, previous day, day before previous and average across all 3-day exposures. We also included temperature and humidity to account for meteorology and used dichotomous percent poverty and gender variables to assess effect modification.

RESULTS

There were 103,974 cases of asthma ED visits during the study period across 39 districts in Lima. We found a 3.7% (95% CI: 1.7%-5.8%) increase in ED visits for every interquartile range (IQR, 6.02 μg/m3) increase in PM2.5 same day exposure with no age stratification. For the 0-18 years age group, we found a 4.5% (95% CI: 2.2%-6.8%) increase in ED visits for every IQR increase in PM2.5 same day exposure. For the 19-64 years age group, we found a 6.0% (95% CI: 1.0%-11.0%) increase in ED visits for every IQR in average 3-day exposure. For the 65 years and up age group, we found a 16.0% (95% CI: 7.0%-24.0%) decrease in ED visits for every IQR increase in PM2.5 average 3-day exposure, although the number of visits in this age group was low (4,488). We found no effect modification by SES or gender.

DISCUSSION

Results from this study provide additional literature on use of satellite-driven exposure estimates in time-series analyses and evidence for the association between PM2.5 and asthma in a low- and middle-income (LMIC) country.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM_2.5 concentrations at moderate scales. The current study calibrated PM_2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R²), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM_2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R² of 0.74, a low RMSE of 16.29 μg × m^-3, and a small MPE of -0.282 μg × m^-3. Meanwhile, the performance of the RF model in inferring the PM_2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM_2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM_2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.

Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) originated in the People's Republic of China in December 2019. Thereafter, a global logarithmic expansion of cases occurred. Some countries have a higher rate of infections despite the early implementation of quarantine. Air pollution might be related to high susceptibility to the virus and associated case fatality rates (deaths/cases*100). Lima, Peru, has the second highest incidence of COVID-19 in Latin America and also has one the highest levels of air pollution in the region.

METHODS

This study investigated the association of levels of PM2.5 exposure in previous years (2010-2016) in 24 districts of Lima with cases, deaths and case fatality rates for COVID-19. Multiple linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020.

RESULTS

There were 128,700 cases in Lima and 2382 deaths due to COVID-19. The case fatality rate was 1.93%. Previous exposure to PM2.5 (2010-2016) was associated with the number of COVID-19- cases (β = 0.07; 95% CI: 0.034-0.107) and deaths (β = 0.0014; 95% CI: 0.0006-0.0.0023) but not with the case fatality rate.

CONCLUSIONS

After adjusting for age, sex and number of food markets, the higher rates of COVID-19 in Metropolitan Lima are attributable to the increased PM2.5 exposure in the previous years, among other reasons. Reduction in air pollution from a long-term perspective and social distancing are needed to prevent the spread of virus outbreaks.

Association between maternal exposure to particulate matter (PM2.5) and adverse pregnancy outcomes in Lima, Peru

The literature shows associations between maternal exposures to PM_2.5 and adverse pregnancy outcomes. There are few data from Latin America. We have examined PM_2.5 and pregnancy outcomes in Lima. The study included 123,034 births from 2012 to 2016, at three public hospitals. We used estimated daily PM_2.5 from a newly created model developed using ground measurements, satellite data, and a chemical transport model. Exposure was assigned based on district of residence (n = 39). Linear and logistic regression analyzes were used to estimate the associations between air pollution exposure and pregnancy outcomes. Increased exposure to PM_2.5 during the entire pregnancy and in the first trimester was inversely associated with birth weight. We found a decrease of 8.13 g (-14.0; -1.84) overall and 18.6 g (-24.4, -12.8) in the first trimester, for an interquartile range (IQR) increase (9.2 µg/m³) in PM_2.5. PM_2.5 exposure was positively associated with low birth weight at term (TLBW) during entire pregnancy (OR: 1.11; 95% CI: 1.03-1.20), and at the first (OR: 1.11; 95% CI: 1.03-1.20), second (OR: 1.09; 95% CI: 1.01-1.17), and third trimester (OR: 1.10; 95% CI: 1.02-1.18) per IQR (9.2 µg/m³) increase. Higher exposure to PM_2.5 was also associated with increased risk of small for gestational age (SGA). There were no statistically significant associations between PM_2.5 exposure and preterm births (PTB). Exposure to higher concentrations of PM_2.5 in Lima may decrease birth weight and increase the frequency of TLBW and SGA. Our study was inconsistent with the literature in finding no associations with preterm birth.

Time-series analysis of ambient PM2.5 and cardiorespiratory emergency room visits in Lima, Peru during 2010-2016

INTRODUCTION

There have been no time-series studies of air pollution in Peru. Here we evaluate the effect of ambient PM_2.5 on emergency room (ER) visits in Lima.

METHODS

We estimated daily PM_2.5 levels at a 1 km² resolution during 2010-2016 using ground measurements, satellite data, and chemical transport model simulations. Population-weighted average daily PM_2.5 levels were calculated for each district in Lima (n = 40), and assigned to patients based on residence. ER visits for respiratory and circulatory diseases were gathered from nine large public hospitals. Poisson regression was used to estimate the rate ratio for daily ER visits with change in daily PM_2.5, controlling for meteorology, time trends, and district.

RESULTS

For each interquartile range (IQR) increase in PM_2.5, respiratory disease ER visits increased 4% (95% CI: 0-5%), stroke visits 10% (3-18%), and ischemic heart disease visits (adults, 18-64 years) 11% (-1, 24%). Districts with higher poverty showed significantly stronger associations of PM_2.5 and respiratory disease ER visits than districts with lower poverty. Effects were diminished 24-42% using Lima-wide instead of district-specific PM_2.5 levels.

CONCLUSIONS

Short-term exposure to ambient PM_2.5 is associated with increases in ER visits in Lima for respiratory diseases and stroke, and among middle-aged adults, ischemic heart disease.

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

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 to 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/m³ PM_2.5 on the day before was significantly associated with daily cardiorespiratory mortality (RR 1.029; 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 cardiorespiratory mortality in Lima, especially for older people. Our data suggest that the existing limits on air pollution exposure are too high.

Revealing the air pollution burden associated with internal Migration in Peru

This study aims to quantify changes in outdoor (ambient) air pollution exposure from different migration patterns within Peru and quantify its effect on premature mortality. Data on ambient fine particulate matter (PM2.5) was obtained from the National Aeronautics and Space Administration (NASA). Census data was used to calculate rates of within-country migration at the district level. We calculated differences in PM2.5 exposure between "current" (2016-2017) and "origin" (2012) districts for each migration patterns. Using an exposure-response relationship for PM2.5 extracted from a meta-analysis, and mortality rates from the Peruvian Ministry of Health, we quantified premature mortality attributable to each migration pattern. Changes in outdoor PM2.5 exposure were observed between 2012 and 2016 with highest levels of PM2.5 in the Department of Lima. A strong spatial autocorrelation of outdoor PM2.5 values (Moran's I = 0.847, p-value=0.001) was observed. In Greater Lima, rural-to-urban and urban-to-urban migrants experienced 10-fold increases in outdoor PM2.5 exposure in comparison with non-migrants. Changes in outdoor PM2.5 exposure due to migration drove 137.1 (95%CI: 93.2, 179.4) premature deaths related to air pollution, with rural-urban producing the highest risk of mortality from exposure to higher levels of ambient air pollution. Our results demonstrate that the rural-urban and urban-urban migrant groups have higher rates of air pollution-related deaths.

Association of PM2.5 concentration with health center outpatient visits for respiratory diseases of children under 5 years old in Lima, Peru

BACKGROUND

Lima is one of the more polluted cities in Latin America. High levels of PM_2.5 have been shown to increase health center outpatient visits of respiratory diseases.

METHODS

Health center outpatient visits for children < 5 years for childhood respiratory disease (acute lower respiratory infections (ALRI), pneumonia and acute bronchiolitis/asthma) from 498 public clinics in Lima were available on a weekly basis from 2011 to 2015 from Peru's Ministry of Health (MINSA). The association between the average weekly concentrations of PM_2.5 was evaluated in relation to the number of weekly health center outpatient visits for children. Weekly PM_2.5 values were estimated using a recently developed model that combined data observed from ground monitors, with data from space satellite and meteorology. Ground monitoring data came from 10 fixed stations of the Peruvian National Service of Meteorology and Hydrology (SENAMHI) and from 6 mobile stations located in San Juan de Miraflores by Johns Hopkins University. We conducted a time-series analysis using a negative binomial model.

RESULTS

We found a significant association between exposure to PM_2.5 and all three types of respiratory diseases, across all age groups. For an interquartile increase in PM_2.5, we found an increase of 6% for acute lower respiratory infections, an increase of 16-19% for pneumonia, and an increase of 10% for acute bronchiolitis / asthma.

CONCLUSIONS

Higher emissions of environmental pollutants such as PM_2,5 could be a trigger for the increase of health center outpatients visits for respiratory diseases (ALRI, pneumonia and asthma), which are themselves risk factors for mortality for children in Lima province, Peru.

Quantifying and spatial disaggregation of air pollution emissions from ground transportation in a developing country context: Case study for the Lima Metropolitan Area in Peru

Ambient air pollution contributes approximately 3.7 million premature deaths annually worldwide with air pollution from ground transportation posing a significant threat in urban areas. This concern is especially relevant in cities with fast-growing economies in the developing countries, as is the case of Lima Metropolitan Area (LMA) in Peru. Currently, there is a limited understanding of the impacts of ground transportation emissions on air pollution and population health in the LMA. In this study we quantified air pollution emissions from ground transportation, by combining local transportation and meteorological data with emission factors determined by the United States Environmental Protection Agency's (US-EPA's) Motor Vehicle Emission Simulator (MOVES). Total annual emissions of carbon monoxide, nitrogen oxides, sulfur dioxide and particulate matter (PM_2.5) were quantified, temporally resolved and then spatially disaggregated within the LMA study domain. Our study, therefore, provides an approach for quantifying transportation emissions for a large metropolitan area in a developing country where detailed data is not available. This research sets the need of future work aiming at understanding the impact of ground transportation emissions, air pollution levels and their subsequent effects on human health. CAPSULE: We provide a framework for computing and spatially disaggregating air pollution emissions from ground transportation in a rapidly growing economy in a developing country context.

Increased Outdoor PM2.5 Concentration Is Associated with Moderate/Severe Anemia in Children Aged 6-59 Months in Lima, Peru

Anemia affects 1.62 billion people worldwide. Although iron deficiency is the main cause of anemia, several other factors may explain its high prevalence. In this study, we sought to analyze the association between outdoor particulate matter PM2.5 levels with anemia prevalence in children aged 6-59 months residing in Lima, Peru (n = 139,368), one of the cities with the worst air pollution in Latin America. The study period was from 2012 to 2016. Anemia was defined according to the World Health Organization (Hb < 11 g/dL). PM2.5 values were estimated by a mathematical model that combined data observed from monitors, with satellite and meteorological data. PM2.5 was analyzed by quintiles. Multiple linear and logistic regressions were used to estimate the associations between hemoglobin concentration (beta) and anemia (odds ratio) with PM2.5, after adjusting by covariates. Prevalence of anemia was 39.6% (95% confidence interval (CI): 39.3-39.9). Mild anemia was observed in 30.8% of children and moderate/severe in 8.84% of children. Anemic children compared with nonanemic children are mainly males, have low body weight, higher rate of stunting, and live in an environment with high PM2.5 concentration. A slight decrease in hemoglobin (4Q B: -0.03, 95% CI: -0.05 to -0.02; 5Q B: -0.04, 95% CI: -0.06 to -0.01) and an increase in the probability of moderate/severe anemia (4Q OR: 1.18, 95% CI: 1.10-1.27; 5Q OR: 1.18, 95% CI: 1.08-1.29) were observed with increased exposure to PM2.5. We conclude that outdoor PM2.5 levels were significantly associated with decreased hemoglobin values and an increase in prevalence of moderate/severe anemia in children under 5 years old.

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

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.