Quantifying health impacts and economic costs of PM2.5 exposure in Mexican cities of the National Urban System

Regionalization of the Mortality Risk from Cardiomyopathy and Respiratory Diseases Based on the Maximum Entropy Model

This research presents a time-series study in one of the most polluted regions in Mexico, the southern part of the Mezquital Valley. Three mortality causes related to areas highly contaminated by industrial activities were considered to carry out this model, namely, ischemic cardiomyopathy, mesothelioma, and pneumoconiosis. The pollutant exposure factors used in the maximum entropy modeling were distance to rivers, distance to industries, particulate matter less than 2.5 microns (PM < 2.5 µm), and the digital elevation model (DEM). A model that expresses the presence of the disease by areas of exposure to pollutants was also obtained. In addition, the odds ratio was calculated to evaluate the level of association of ischemic cardiomyopathy (OR = 3.37 and 95% CI: 3.05-3.6) and mesothelioma (OR = 4.79 and 95% CI: 3.5-6.08) by areas of exposure. In the case of pneumoconiosis, only cases in the very high exposure category were recorded, so it was not comparable with the remaining areas. It is important to mention that particulate matter in the municipalities of the Mezquital Valley presented values above 20 μg/m3 and that in accordance with the provisions of the Norma Oficial Mexicana de Salud Ambiental or NOM (translated as Mexican Official Standard for Environmental Health) and the Agency for Toxic Substances and the Disease Registry (ATSDR), high concentrations of particulate matter can have a severe impact on the development of some diseases. In the studied area, ischemic cardiomyopathy and mesothelioma were attributed to pollution in 70.3% and 79.1%, respectively; therefore, pollution mitigation could prevent the occurrence of these two diseases.

Gender disparities in lost productivity resulting from non-communicable diseases in Mexico, 2005-2021

Background

Non-communicable diseases (NCDs) cause long-term impacts on health and can substantially affect people's ability to work. Little is known about how such impacts vary by gender, particularly in low- and middle-income countries (LMICs), where productivity losses may affect economic development. This study assessed the long-term productivity loss caused by major NCDs among adult women and men (20-76 years) in Mexico because of premature death and hospitalisations, between 2005 and 2021.

Methods

We conducted an economic valuation based on the Human Capital Approach. We obtained population-based data from the National Employment Survey from 2005 to 2021 to estimate the expected productivity according to age and gender using a two-part model. We utilised expected productivity based on wage rates to calculate the productivity loss, employing Mexican official mortality registries and hospital discharge microdata for the same period. To assess the variability in our estimations, we performed sensitivity analyses under two different scenarios.

Results

Premature mortality by cancers, diabetes, chronic cardiovascular diseases (CVD), chronic respiratory diseases (CRD) and chronic kidney disease (CKD) caused a productivity loss of 102.6 billion international US dollars (Intl. USD) from 2.8 million premature deaths. Seventy-three percent of this productivity loss was observed among men. Cancers caused 38.3% of the productivity loss (mainly among women), diabetes 38.1, CVD 15.1, CRD 3.2, and CKD 5.3%. Regarding hospitalisations, the estimated productivity loss was 729.7 million Intl. USD from 54.2 million days of hospitalisation. Men faced 65.4 and women 34.6% of these costs. Cancers caused 41.3% of the productivity loss mainly by women, followed by diabetes (22.1%), CKD (20.4%), CVD (13.6%) and CRD (2.6%).

Conclusions

Major NCDs impose substantial costs from lost productivity in Mexico and these tend to be higher amongst men, while for some diseases the economic burden is higher for women. This should be considered to inform policymakers to design effective gender-sensitive health and social protection interventions to tackle the burden of NCDs.

Health and Economic Benefits of Complying With the World Health Organization Air Quality Guidelines for Particulate Matter in Nine Major Latin American Cities

Objectives

This study aims to estimate the short-term preventable mortality and associated economic costs of complying with the World Health Organization (WHO) air quality guidelines (AQGs) limit values for PM10 and PM2.5 in nine major Latin American cities.

Methods

We estimated city-specific PM-mortality associations using time-series regression models and calculated the attributable mortality fraction. Next, we used the value of statistical life to calculate the economic benefits of complying with the WHO AQGs limit values.

Results

In most cities, PM concentrations exceeded the WHO AQGs limit values more than 90% of the days. PM10 was found to be associated with an average excess mortality of 1.88% with concentrations above WHO AQGs limit values, while for PM2.5 it was 1.05%. The associated annual economic costs varied widely, between US$ 19.5 million to 3,386.9 million for PM10, and US$ 196.3 million to 2,209.6 million for PM2.5.

Conclusion

Our findings suggest that there is an urgent need for policymakers to develop interventions to achieve sustainable air quality improvements in Latin America. Complying with the WHO AQGs limit values for PM10 and PM2.5 in Latin American cities would substantially benefits for urban populations.

Value per Statistical Life at the Sub-National Level as a Tool for Assessing Public Health and Environmental Problems

This article aims to estimate the Value per Statistical Life (VSL) and Value per Statistical Life Year (VSLY) at the sub-national level, which can be used to calculate the economic impact of health and environmental problems. We estimate the value of life for Mexico and its 32 states, grouped into 5 regions for 2021. We used the OECD's guidelines on "Mortality Risk Valuation in Environment, Health and Transport Policies," which applies the measure of Willingness to Pay (WTP) and Cost-Benefit Analysis (CBA). Mexico's overall VSL of $2 000 000 USD in 2021 showcases the value placed on human life. The variation in VSL across the 32 states, with Chiapas having the lowest VSL of $400 000 USD and Mexico City boasting the highest VSL of $3 300 000 USD highlights the different levels of regional development and people's willingness to pay to reduce the risk of mortality. Our estimates of VSL and VSLY have the potential to contribute to the evaluation of public policies in the fields of health and the environment. Monetizing human life through these estimates can offer valuable insights to policymakers at both the national and sub-national levels. By quantifying the economic value placed on human life, this paper helps decision-makers prioritize investments, assess the cost-effectiveness of interventions, and allocate resources to maximize societal well-being.

Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China

Ambient pollutants, particularly fine particulate matter (PM2.5) and ozone (O3), pose significant risks to both public health and economic development. In recent years, PM2.5 concentration in China has decreased significantly, whereas that of O3 has increased rapidly, leading to considerable health risks. In this study, a generalized additive model was employed to establish the relationship of PM2.5 and O3 exposure with non-accidental mortality across 17 districts and counties in Jilin Province, China, over 2015-2016. The health burden and economic losses attributable to PM2.5 and O3 were assessed using high-resolution satellite and population data. According to the results, per 10 µg/m3 increase in PM2.5 and O3 concentrations related to an overall relative risk (95% confidence interval) of 1.004 (1.001-1.007) and 1.009 (1.005-1.012), respectively. In general, the spatial distribution of mortality and economic losses was uneven. Throughout the study period, a total of 23,051.274 mortalities and 27,825.015 million Chinese Yuan (CNY) in economic losses were attributed to O3 exposure, which considerably surpassing the 5,450.716 mortalities and 6,553,780 million CNY in economic losses attributed to PM2.5 exposure. The O3-related health risks and economic losses increased by 3.75% and 9.3% from 2015 to 2016, while those linked to PM2.5 decreased by 23.33% and 18.7%. Sensitivity analysis results indicated that changes in pollutant concentrations were the major factors affecting mortality rather than baseline mortality and population.

Heavy metal contamination of soil, sediment and water due to galena mining in Ebonyi State Nigeria: Economic costs of pollution based on exposure health risks

In this study, the effect of galena mining with respect to heavy metal pollution of groundwater, surface water, soil and sediments in Enyimagalagu and Mkpuma-Akpatakpa Communities in Ebonyi State, Nigeria, was investigated to estimate the health and economic burden. The heavy metals were measured using atomic absorption spectrometry (AAS). The water quality of the groundwater and surface water was determined using the water quality index (WQI). The potential health burden was estimated by determining carcinogenic and non-carcinogenic health risks via risk index (RI) and hazard index (HI), respectively. The potential economic burden was estimated using the value of statistical life (VSL) and cost of illness (COI). Obtained results revealed that the WQI of the groundwater and surface water ranged from 197 to 327 indicating very poor to unsuitable water for drinking. No non-carcinogenic risks were associated with exposure to the soil and sediment components of the environment. However, there were non-carcinogenic risks associated with the surface and groundwater, with the HIs ranging from 1.8 to 5.4. Based on the carcinogenic risk threshold of 10^-6, there were carcinogenic risks across all the environmental media ranging from 8.4*10^-5 - 2.1*10^-3. The economic cost of heavy metal pollution ranged from 20.7 million USD (10.35 billion) to 543.3 million USD (271.6 billion) for the VSL, whereas it ranged from 0.141 million USD (70.8 million) to 3.72 million USD (1.86 billion) for the COI. These costs are quite huge and can be avoided via pollution abatement measures.

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

BACKGROUND

Machine-learning algorithms are becoming popular techniques to predict ambient air PM_2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM_2.5 concentrations (mean PM_2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM_2.5).

OBJECTIVE

Our goal was to develop a machine-learning model to predict mean PM_2.5 and max PM_2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019.

METHODS

We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM_2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM_2.5 and heat, compliance with local air-quality standards, and the relationship of PM_2.5 exposure with social marginalization.

RESULTS

Our models for mean and max PM_2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m³, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m³. In 2010, everybody in the study region was exposed to unhealthy levels of PM_2.5. Hotter days had greater PM_2.5 concentrations. Finally, we found similar exposure to PM_2.5 across levels of social marginalization.

SIGNIFICANCE

Machine learning algorithms can be used to predict highly spatiotemporally resolved PM_2.5 concentrations even in regions with sparse monitoring.

IMPACT

Our PM_2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Species-specific efficiency in PM2.5 removal by urban trees: From leaf measurements to improved modeling estimates

The growing population in cities is causing a deterioration of air quality due to the emission of pollutants, causing serious health impacts. Trees and urban forests can contribute through the interception and removal of air pollutants such as particulate matter (PM). The dry deposition of PM by vegetation depends on air pollutant concentration, meteorological conditions, and specific leaf traits. Several studies explored the ability of different plant species to accumulate PM on leaf structures leading to the development of models to quantify the PM removal. The i-Tree Eco is the most used model to evaluate ecosystem services provided by urban trees. However, fine particulate matter (PM_2.5) removal is still calculated with a poorly evaluated function of deposition velocity (which depends on wind speed and leaf area) without differentiating between tree species. Therefore, we present an improvement of the standard model calculation introducing a leaf trait index to distinguish the species effect on PM net removal. We also compared model results with measurements of deposited leaf PM by vacuum filtration. The index includes the effect of morphological and functional leaf characteristics of tree species using four parameters: leaf water storage, deposition velocity, resuspension rate and leaf washing capacity. Leaves of 11 common urban tree species were sampled in representative areas of the city of Ferrara (Italy) and at different times of the year from 2018 to 2021. This includes four deciduous broadleaf trees (Tilia cordata, Platanus acerifolia, Acer platanoides, Celtis australis), three evergreen broadleaf trees (Quercus ilex, Magnolia grandiflora, Nerium oleander), and four conifers (Thuja orientalis, Cedrus libani, Pinus pinaster, Picea abies). The results provide significant advancement in assessing PM removal using decision support tools such as models to properly select tree species for future urban tree planting programs aimed at improving air quality.

Legacy PAHs in effluent receiving river sediments near a large petroleum products depot in Enugu, Nigeria: Human health risks and economic cost of pollution

This study assessed the human health risk of exposure to legacy PAHs in the Nwaenebo River sediments that received effluents for over two decades from the Nigeria National Petroleum Corporation (NNPC) petroleum product Depot in Emene, Enugu, Nigeria. The study went further to estimate economic costs of the sediment PAHs pollution based on the human health risk of exposure. The human health risks were determined by estimating carcinogenic and mutagenic risks via Benzo[a]pyrene total potential equivalent (BaP TPE) and mutagenic equivalent quotient (MEQ). The economic costs of the sediment pollution comprised costs due to mortality and those due to morbidity and were estimated using the value of statistical lives (VSLs) and cost of illness (CoI), respectively. The study, with an appropriate selection of sampling points established that the NNPC petroleum Depot was responsible for the Nwaenebo River sediment PAHs pollution with ƩPAHs concentration 14.3-163 mg/kg. The carcinogenic and mutagenic risks varied from 1.3*10^-5 to 4.7*10^-5 and 1.4*10^-5 to 6.0*10^-5 respectively. Based on risk threshold of 10^-6, these risks were high. The long term economic costs of pollution of the sediments by the PAHs were estimated at 60.5 million USD and 0.46 million USD for mortality and morbidity costs, respectively.

Effects of air pollution on human health - Mechanistic evidence suggested by in vitro and in vivo modelling

Airborne particulate matter (PM) comprises both solid and liquid particles, including carbon, sulphates, nitrate, and toxic heavy metals, which can induce oxidative stress and inflammation after inhalation. These changes occur both in the lung and systemically, due to the ability of the small-sized PM (i.e. diameters ≤2.5 μm, PM_2.5) to enter and circulate in the bloodstream. As such, in 2016, airborne PM caused ∼4.2 million premature deaths worldwide. Acute exposure to high levels of airborne PM (eg. during wildfires) can exacerbate pre-existing illnesses leading to hospitalisation, such as in those with asthma and coronary heart disease. Prolonged exposure to PM can increase the risk of non-communicable chronic diseases affecting the brain, lung, heart, liver, and kidney, although the latter is less well studied. Given the breadth of potential disease, it is critical to understand the mechanisms underlying airborne PM exposure-induced disorders. Establishing aetiology in humans is difficult, therefore, in-vitro and in-vivo studies can provide mechanistic insights. We describe acute health effects (e.g. exacerbations of asthma) and long term health effects such as the induction of chronic inflammatory lung disease, and effects outside the lung (e.g. liver and renal change). We will focus on oxidative stress and inflammation as this is the common mechanism of PM-induced disease, which may be used to develop effective treatments to mitigate the adverse health effect of PM exposure.

Long-Term Exposure to Ozone and Fine Particulate Matter and Risk of Premature Coronary Artery Disease: Results from Genetics of Atherosclerotic Disease Mexican Study

(1) Background: Epidemiological studies have identified associations between fine particulate matter (PM2.5) and ozone exposure with cardiovascular disease; however, studies linking ambient air pollution and premature coronary artery disease (pCAD) in Latin America are non-existing. (2) Methods: Our study was a case−control analysis nested in the Genetics of Atherosclerotic Disease (GEA) Mexican study. We included 1615 participants (869 controls and 746 patients with pCAD), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. We defined pCAD as history of myocardial infarction, angioplasty, revascularization surgery or coronary stenosis > 50% diagnosed before age 55 in men and age 65 in women. Controls were healthy individuals without personal or family history of pCAD and with coronary artery calcification equal to zero. Hourly measurements of ozone and PM2.5 from the Atmospheric Monitoring System in Mexico City (SIMAT in Spanish; Sistema de Monitero Atmosférico de la Ciudad de México) were used to calculate annual exposure to ozone and PM2.5 in the study participants. (3) Results: Each ppb increase in ozone at 1-year, 2-year, 3-year and 5-year averages was significantly associated with increased odds (OR = 1.10; 95% CI: 1.03−1.18; OR = 1.17; 95% CI: 1.05−1.30; OR = 1.18; 95% CI: 1.05−1.33, and OR = 1.13; 95% CI: 1.04−1.23, respectively) of pCAD. We observed higher risk of pCAD for each 5 µg/m3 increase only for the 5-year average of PM2.5 exposure (OR = 2.75; 95% CI: 1.47−5.16), compared to controls. (4) Conclusions: Ozone exposure at different time points and PM2.5 exposure at 5 years were associated with increased odds of pCAD. Our results highlight the importance of reducing long-term exposure to ambient air pollution levels to reduce the burden of cardiovascular disease in Mexico City and other metropolitan areas.

Evaluating PM2.5 -Related health costs in China-Evidence from 140 Chinese cities

INTRODUCTION

In recent years, China's economy has grown rapidly, and the health condition of Chinese residents has significantly improved. However, this rapid economic and social development has also brought a series of environmental problems, such as serious haze pollution, of which the main contents are PM_2.5 particles. The objective of this study is to quantitatively estimate the PM_2.5 -related health costs in China.

METHODS

Based on city-level data from 140 major Chinese cities as well as the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta city clusters in 2010, the value of a statistical life method based on willingness to pay was employed. Moreover, global and local Moran's I values were calculated to examine the spatial distribution of the health cost of haze pollution in China.

RESULTS

In areas with heavy haze pollution or a high level of economic development, residents' health costs will also be higher. In addition, there is a spatial aggregation phenomenon in the spatial distribution of health costs in China, which is mainly in the form of "high-high" aggregation, with high-value cities converging with other high-value cities.

CONCLUSIONS

The health cost of haze pollution in China is very considerable, and there are regional differences.

Health and Economic Impacts Assessment of O3 Exposure in Mexico

Health effects related to exposure to air pollution such as ozone (O₃) have been documented. The World Health Organization has recommended the use of the Sum of O₃ Means Over 35 ppb (SOMO35) to perform Health Impact Assessments (HIA) for long-term exposure to O₃. We estimated the avoidable mortality associated with long-term exposure to tropospheric O₃ in 14 cities in Mexico using information for 2015. The economic valuation of avoidable deaths related to SOMO35 exposure was performed using the willingness to pay (WTP) and human capital (HC) approaches. We estimated that 627 deaths (95% uncertainty interval (UI): 227–1051) from respiratory diseases associated with the exposure to O₃ would have been avoided in people over 30 years in the study area, which confirms the public health impacts of ambient air pollution. The avoidable deaths account for almost 1400 million USD under the WTP approach, whilst the HC method yielded a lost productivity estimate of 29.7 million USD due to premature deaths. Our findings represent the first evidence of the health impacts of O₃ exposure in Mexico, using SOMO35 metrics.

Prenatal Particulate Matter (PM) Exposure and Natriuretic Peptides in Newborns from Mexico City

(1) Background: The aim of this study was to assess associations between particulate matter (PM) exposure and natriuretic peptide concentrations in cord blood from newborns. (2) Methods: we conducted a cross-sectional study in Mexico City with 101 pregnant women from CIMIGEN Hospital. Atrial natriuretic peptide (ANP), B-type natriuretic peptide (BNP) and C-type natriuretic peptide (CNP) were measured in plasma from cord blood in 51 newborns by ELISA. We estimated PM exposure (PM2.5 and PM10) at first, second and third trimester of pregnancy. (3) Results: The median and interquartile range for ANP, BNP and CNP plasma concentrations were 66.71 (46.92-80.23), 98.23 (73.64-112.30) and 1129.11 (944.10-1452.02) pg/mL, respectively. PM2.5 and PM10 levels for the whole pregnancy period were 22.2 µg/m³ and 41.63 µg/m³, respectively. Employing multivariable linear regression models adjusted for maternal age, newborn sex, smoking before pregnancy, maternal occupation and newborns' length and height, we observed a 2.47 pg/mL (95%CI: -4.67, -0.27) decrease in BNP associated with PM2.5 exposure during second trimester. Adjusted for the same set of confounders, third trimester PM10 exposure was inversely associated with ANP concentrations (beta estimate: -0.90; 95% CI: -1.80, -0.03). Neither PM10 nor PM2.5 were associated with CNP at any trimester of pregnancy. (4) Conclusions: Prenatal exposure to particulate matter was associated with ANP and BNP decrease in newborns.

Particulate Matter, an Intrauterine Toxin Affecting Foetal Development and Beyond

Air pollution is the 9th cause of the overall disease burden globally. The solid component in the polluted air, particulate matters (PMs) with a diameter of 2.5 μm or smaller (PM_2.5) possess a significant health risk to several organ systems. PM_2.5 has also been shown to cross the blood–placental barrier and circulate in foetal blood. Therefore, it is considered an intrauterine environmental toxin. Exposure to PM_2.5 during the perinatal period, when the foetus is particularly susceptible to developmental defects, has been shown to reduce birth weight and cause preterm birth, with an increase in adult disease susceptibility in the offspring. However, few studies have thoroughly studied the health outcome of foetuses due to intrauterine exposure and the underlying mechanisms. This perspective summarises currently available evidence, which suggests that intrauterine exposure to PM_2.5 promotes oxidative stress and inflammation in a similar manner as occurs in response to direct PM exposure. Oxidative stress and inflammation are likely to be the common mechanisms underlying the dysfunction of multiple systems, offering potential targets for preventative strategies in pregnant mothers for an optimal foetal outcome.

Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps.