Particulate matter levels in a South American megacity: the metropolitan area of Lima-Callao, Peru

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.

Influence of the geographic location and house characteristics on the concentration of microplastics in indoor dust

Microplastics (MPs) are known for their ubiquity, having been detected in virtually any environmental compartment. However, indoor MPs concentrations are poorly studied despite being closely related to human exposure. The present study aims to evaluate the presence of MPs in settled atmospheric dust in 60 houses distributed in 12 districts of the metropolitan city of Lima, Peru, and investigate the influence of their geographical location and house characteristics. MPs concentration ranged from 0.01 to 33.9 MPs per mg of dust. Fibers and blue were the most frequent shape and color (98 % and 69 %, respectively). Also, 82 % of the particles were between 500 μm - 5 mm in size. A higher concentration of MPs was identified in the center-south of the city. The houses located on the highest floors (levels 4 to 13 to ground) displayed higher concentrations. MPs were primarily composed of polyester (PET), polypropylene (PP), and ethylene-vinyl acetate (EVA), among others. The polymers identified suggest that MPs derived from the fragmentation of components frequently found in houses, such as synthetic clothing, food storage containers, toys, carpets, floors, and curtains. The incorporation of MPs from the outside into dwellings is not ruled out. Future studies should evaluate the influence of consumption habits and housing characteristics on the abundance of MPs.

A machine learning approach to analyse ozone concentration in metropolitan area of Lima, Peru

The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM$$_{10}$$ 10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM$$_{10}$$ 10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM$$_{10}$$ 10 for San Juan de Miraflores (SJM) (PM$$_{10}$$ 10 -SJM: 78.7 $$\upmu$$ μ g/m$$^{3}$$ 3 ) and the lowest in Santiago de Surco (SS) (PM$$_{10}$$ 10 -SS: 40.2 $$\upmu$$ μ g/m$$^{3}$$ 3 ). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM$$_{10}$$ 10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM$$_{10}$$ 10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM$$_{10}$$ 10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$<0.3$$ < 0.3 ) and the NSE-MLR criterion (0.3804) was acceptable. PM$$_{10}$$ 10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.

Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes

La Oroya is a city in the Peruvian Andes that has suffered a serious deterioration in its air quality, especially due to the high rate of sulfur dioxide (SO2) emissions, which underlines the importance of knowing its sources of contamination and variation over the years. In this sense, this study aimed to evaluate the immission levels and determine the sources of SO2 contamination in La Oroya. This analysis was performed using the hourly concentration data of SO2, and meteorological variables (wind speed and direction), which were analyzed for a period of three years (2018-2020). Graphs of time series, wind and pollutant roses, bivariate polar graphs, clustering k-means, nonparametric statistical tests, and the application of the conditional bivariate probability function were performed to analyze the data and identify the emission sources. The mean concentration of SO2 was 264.2 μg m-3 for the study period, where 55.66 and 2.37% of the evaluated days exceeded the guideline values recommended by the World Health Organization and the Peruvian Environmental Quality Standard for air for 24 h, respectively. The results showed a defined pattern for the daily and monthly variations, with peaks in the morning hours (0900-1000 h LT) and at the end of the year (December), respectively. The main sources of SO2 emissions identified were light and heavy vehicles that travel through the Central Highway, the La Oroya Metallurgical Complex, the transit of vehicles within the city, and the diesel-electric locomotives that provide cargo transportation services and tourism passenger transportation. The article attempts to contribute to the development of adequate air quality management policies.

The Effect of Urban Green Spaces on Reduction of Particulate Matter Concentration

In an urban scenario, one of the air pollutants most harmful to human health and environmental is the particulate matter (PM). Considering that urban green areas can contribute to mitigating the effects of PM, this work compares the concentration of PM_2.5 in two closer locations in Rio de Janeiro, in order to verify how vegetation cover can actually improve air quality. One is the entrance to the Rebouças Tunnel (RT) and the other is the Rio de Janeiro Botanical Garden (RJBG). For this purpose, PM_2.5 samples were taken from September 2017 to March 2018 using a Large Volume Sampler (LVS). The results reveal that RT has a higher concentration of PM_2.5 in almost all samples. The RJBG obtains concentrations around 33% less than the other area, suggesting that the presence of urban green areas like the RJBG can reduce PM_2.5 levels when compared to places with less vegetation cover, providing better air quality.

Influence of number of visitors and weather conditions on airborne particulate matter mass concentrations at the Plitvice Lakes National Park, Croatia during summer and autumn

We investigated the influence of local meteorological conditions and number of visitors on ambient particulate matter (PM) mass concentrations and particle fraction ratios at the Plitvice Lakes National Park between July and October 2018. Outdoor mass concentrations of particles with aerodynamic diameters of less than 1, 2.5, and 10 μm (PM₁, PM_2.5, and PM₁₀, respectively) and indoor PM₁ were measured with two light-scattering laser photometers set up near the largest and most visited Kozjak Lake. Our findings suggest that the particles mainly originated from background sources, although some came from local anthropogenic activities. More specifically, increases in both indoor and outdoor mass concentrations coincided with the increase in the number of visitors. Indoor PM₁ concentrations also increased with increase in outdoor air temperature, while outdoor PMs exhibited U-shaped dependence (i.e., concentrations increased only at higher outdoor air temperatures). This behaviour and the decrease in the PM₁/PM_2.5 ratio with higher temperatures suggests that the production and growth of particles is influenced by photochemical reactions. The obtained spectra also pointed to a daily but not to weekly periodicity of PM levels.

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city's economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of [Formula: see text] based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate [Formula: see text] concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.

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.

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.

Geochemical Characterization and Heavy Metal Sources in PM10 in Arequipa, Peru

Particulate matter smaller than 10 μm (PM10) is an important air pollutant that adversely affects human health by increasing the risk of respiratory and cardiovascular diseases. Recent studies reported multiple extreme PM10 levels at high altitude Peruvian cities, which resulted from a combination of high emissions and limited atmospheric circulation at high altitude. However, the emission sources of the PM10 still remain unclear. In this study, we collected PM10 samples from four sites (one industrial site, one urban site, and two rural sites) at the city of Arequipa, Peru, during the period of February 2018 to December 2018. To identify the origins of PM10 at each site and the spatial distribution of PM10 emission sources, we analyzed major and trace element concentrations of the PM10. Of the observed daily PM10 concentrations at Arequipa during our sampling period, 91% exceeded the World Health Organization (WHO) 24-h mean PM10 guideline value, suggesting the elevated PM10 strongly affected the air quality at Arequipa. The concentrations of major elements, Na, K, Mg, Ca, Fe, and Al, were high and showed little variation, suggesting that mineral dust was a major component of the PM10 at all the sites. Some trace elements, such as Mn and Mo, originated from the mineral dust, while other trace elements, including Pb, Sr, Cu, Ba, Ni, As and V, were from additional anthropogenic sources. The industrial activities at Rio Seco, the industrial site, contributed to significant Pb, Cu, and possibly Sr emissions. At two rural sites, Tingo Grande and Yarabamba, strong Cu emissions were observed, which were likely associated with mining activities. Ni, V, and As were attributed to fossil fuel combustion emissions, which were strongest at the Avenida Independencia urban site. Elevated Ba and Cu concentrations were also observed at the urban site, which were likely caused by heavy traffic in the city and vehicle brake wear emissions.

From the Andes to the desert: 16S rRNA metabarcoding characterization of aquatic bacterial communities in the Rimac river, the main source of water for Lima, Peru

The Rimac river is the main source of water for Lima, Peru's capital megacity. The river is constantly affected by different types of contamination including mine tailings in the Andes and urban sewage in the metropolitan area. In this work, we aim to produce the first characterization of aquatic bacterial communities in the Rimac river using a 16S rRNA metabarcoding approach which would be useful to identify bacterial diversity and potential understudied pathogens. We report a lower diversity in bacterial communities from the Lower Rimac (Metropolitan zone) in comparison to other sub-basins. Samples were generally grouped according to their geographical location. Bacterial classes Alphaproteobacteria, Bacteroidia, Campylobacteria, Fusobacteriia, and Gammaproteobacteria were the most frequent along the river. Arcobacter cryaerophilus (Campylobacteria) was the most frequent species in the Lower Rimac while Flavobacterium succinicans (Bacteroidia) and Hypnocyclicus (Fusobacteriia) were the most predominant in the Upper Rimac. Predicted metabolic functions in the microbiota include bacterial motility and quorum sensing. Additional metabolomic analyses showed the presence of some insecticides and herbicides in the Parac-Upper Rimac and Santa Eulalia-Parac sub-basins. The dominance in the Metropolitan area of Arcobacter cryaerophilus, an emergent pathogen associated with fecal contamination and antibiotic multiresistance, that is not usually reported in traditional microbiological quality assessments, highlights the necessity to apply next-generation sequencing tools to improve pathogen surveillance. We believe that our study will encourage the integration of omics sciences in Peru and its application on current environmental and public health issues.

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

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 PM_2.5 exposure in the previous years (2010-2016) in 24 districts of Lima with the positive-cases, deaths and case-fatality rates of 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 in Lima 128,700 SARS-CoV-2 positive cases, and 2,382 deaths due to COVID-19. The case-fatality rate was 1.93%. Previous exposure to PM_2.5 (years 2010-2016) was associated with number of Covid-19 positive-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 case-fatality rate.

Conclusions

the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM_2.5 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.

Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

The sanitary measures implemented to control and prevent an increase in infections due to the COVID-19 pandemic have produced an improvement in the air quality of many urban areas around the world. We assessed air quality during the COVID-19 pandemic for particulate matter (PM_2.5 and PM₁₀), NO₂ and O₃ in in metropolitan area of Lima, Peru between pre-lockdown period (February 1 and March 15 of 2020), historical period (March 16 to April 30 2017-2019) and lockdown period (March 16 to April 30, 2020). The complete national lockdown that was implemented in Peru produced statistically significant reductions in the in-air pollutant (PM₁₀ (-40% and -58%), PM_2.5 (-31% and -43%) and NO₂ (-46% and -48%)), as recorded by the by the ground-based air quality monitoring network throughout the metropolitan area, compared with the corresponding concentrations for the previous weeks and over the same period for 2017-2019. Analysis of the spatial Distribution of satellite data also show decreases in the concentrations of PM_10, PM_2.5 and NO₂ as a result of the containment measures and suspension of activities implemented by the Peruvian government. The concentrations of O₃ significantly increased (11% and 170%) as a result of the decrease in the concentration of NO₂, confirming that the study area is a hydrocarbon-limited system, as previously reported. The results obtained contribute to the assessment by the regulatory agencies of the possible strategies of control and monitoring of air pollution in the study area.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-00990-3.

Characterising particulate matter source contributions in the pollution control zone of mining and related industries using bivariate statistical techniques

Na Phra Lan Subdistrict is a pollution control zone with the highest PM10 level in Thailand. Major mobile and industrial sources in the area are related to stone crushing, quarrying and mining. This study used statistical techniques to investigate the potential sources influencing high PM10 levels in Na Phra Lan. Hourly PM10 data and related parameters (PM2.5, PMcoarse and NOx) from 2014-2017 were analysed using time series, bivariate polar plot and conditional bivariate probability function (CBPF). Results of diurnal variation revealed two peaks of PM10 levels from 06:00-10:00 and 19:00-23:00 every month. For seasonal variation, high PM10 concentrations were found from October to February associated with the cool and dry weather during these months. The bivariate polar plot and CBPF confirmed two potential sources, i.e., resuspended dust from mobile sources close to the air quality monitoring station (receptor) and industrial sources of mining, quarrying and stone crushing far from the station on the northeast side. While the industrial source areas played a role in background PM10 concentrations, the influence of mobile sources increased the concentrations resulting in two PM10 peaks daily. From the study results, we proposed that countermeasure activities should focus on potential source areas, resuspended road dust from vehicles and the industrial sources related to quarrying and mining, rather than distributing equal attention to all sources.

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.

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.

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.

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

It is well recognized that exposure to fine particulate matter (PM_2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM_2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM_2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM_2.5 concentrations at a 1 km² spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R² (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m³). Mean PM_2.5 for ground measurements was 24.7 μg/m³ while mean estimated PM_2.5 was 24.9 μg/m³ in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m³ (Std.Dev. = 5.97 μg/m³), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM_2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM_2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM_2.5 measurements at 1 km² spatial resolution to support future epidemiological studies.

Chemical Characterization of PM2.5 at Rural and Urban Sites around the Metropolitan Area of Huancayo (Central Andes of Peru)

The purpose of this study was to determine PM2.5 mass concentration and the contents of trace elements and water-soluble ions in samples collected inside the Metropolitan area of Huancayo. Four monitoring stations were installed at three urban areas (UNCP, HYO, and CHI) and one rural (IGP). The sampling campaign was carried out from March 2017 to November 2017. The PM2.5 content was determined by gravimetric method, and fifteen trace elements (TE) and seven water-soluble ions were detected by inductively coupled plasma mass spectrometry (ICP–MS), and ion chromatography (IC), respectively. Datasets were assessed by one ANOVA test to detect significant differences among monitoring station. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied for source identification. The mean annual concentration of PM2.5 mass concentrations has ranged (average) from 3.4 to 36.8 µg/m3 (16.6 ± 6.8 µg/m3) for the monitoring stations under study. The annual World Health Organization thresholds and national air quality standards were exceeded. Significant differences (p < 0.05) were observed between most trace elements at urban and rural areas. PCA and HCA illustrated that the most important sources of traces element originated of natural origin (soil re-suspension) and vehicular sources (fuel combustion, abrasion of vehicles tires, wear car components).

In utero exposure to fine particulate matter results in an altered neuroimmune phenotype in adult mice

Environmental exposure to air pollution has been linked to a number of health problems including organ rejection, lung damage and inflammation. While the deleterious effects of air pollution in adult animals are well documented, the long-term consequences of particulate matter (PM) exposure during animal development are uncertain. In this study we tested the hypothesis that environmental exposure to PM 2.5 μm in diameter in utero promotes long term inflammation and neurodegeneration. We evaluated the behavior of PM exposed animals using several tests and observed deficits in spatial memory without robust changes in anxiety-like behavior. We then examined how this affects the brains of adult animals by examining proteins implicated in neurodegeneration, synapse formation and inflammation by western blot, ELISA and immunohistochemistry. These tests revealed significantly increased levels of COX2 protein in PM2.5 exposed animal brains in addition to changes in synaptophysin and Arg1 proteins. Exposure to PM2.5 also increased the immunoreactivity for GFAP, a marker of activated astrocytes. Cytokine concentrations in the brain and spleen were also altered by PM2.5 exposure. These findings indicate that in utero exposure to particulate matter has long term consequences which may affect the development of both the brain and the immune system in addition to promoting inflammatory change in adult animals.