High Concentrations Of Air Pollutants Research Articles

Risks of preterm birth and low birth weight and maternal exposure to NO2/PM2.5 acquired by dichotomous evaluation: a systematic review and meta-analysis.

No consistent results from past studies have been found on the relationship between the effects of air pollutant exposure, preterm birth (PTB) and low birth weight (LBW) in fetuses. This study aimed to analyze the impact of high concentrations of air pollutants on the health outcomes of fetuses, especially regarding PTB and LBW. This study used keywords related to air pollutants, pregnancy, and birth outcomes, to search the literature within the databases of the Cochrane Library, PubMed, and Embase, which were published as of July 26, 2022. A total of 24 studies were included in this meta-analysis. This meta-analysis revealed that nitrogen dioxide (NO2) exposure throughout pregnancy was associated with an increased risk of PTB. Maternal exposure to PM2.5 (particulate matter sized less than 2.5 μm) during gestation was associated with the risk of LBW. The findings of this meta-analysis provide an important foundation for evaluating the relationship between exposure of air pollutants and fetal birth outcomes in countries with severe air pollution in the future.

PM2.5 forecasting for an urban area based on deep learning and decomposition method

Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.

The impact of PM2.5, PM10 and NO2 on Covid-19 severity in a sample of patients with multiple sclerosis: A case-control study

BackgroundMany studies investigated the association between air pollution and Covid-19 severity but the only study focusing on patients with Multiple Sclerosis (MS) exclusively evaluated exposure to PM2.5. We aim to study, in a sample of MS patients, the impact of long-term exposure to PM2.5, PM10 and NO2 on Covid-19 severity, described as occurrence of pneumonia. MethodsA 1:2 ratio case-control study was designed, differentiating cases and controls based on Covid-19 pneumonia. Associations between pollutants and outcome were studied using logistic regression. Weighted quantile sum (WQS) logistic regression was used to identify the individual contribution of each pollutant within the mixture; Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression was performed to confirm the variable selection from WQS. All the analyses were adjusted for confounders selected a priori. ResultsOf the 615 eligible patients, 491 patients provided detailed place of exposure and were included in the principal analysis. Higher concentrations of air pollutants were associated with increased odds of developing Covid-19 pneumonia (PM2.5: 3rd vs 1st tercile OR(95% CI)=2.26(1.29;3.96); PM10: 3rd vs 1st tercile OR(95% CI)=2.12(1.22;3.68); NO2: 3rd vs 1st tercile OR(95% CI)=2.12(1.21;3.69)). Pollutants were highly correlated with each other; WQS index was associated to an increased risk of pneumonia (β=0.44; p-value=0.004) and the main contributors to this association were NO2 (41%) and PM2.5 (34%). Consistently, Lasso method selected PM2.5 and NO2. ConclusionsHigher long-term exposure to PM2.5, PM10 and NO2 increased the odds of Covid-19 pneumonia among MS patients and the most dangerous pollutants were NO2 and PM2.5.

Health Impact Assessment from Rice Straw Production in Cambodia

Rice is a staple crop best known for its crucial role in feeding humans. In Cambodia, the human population depends on rice as its staple food. Increased rice production results in an increase in straw residue. The extensive residue caused by straw burning significantly contributes to high concentrations of air pollution, which are associated with critical health issues. As such, our objective in this study was to assess the human health impact of rice straw production in Cambodia. In this assessment, we focused on primary and secondary fine particulate matter formation (PM2.5, NH3, NOx, and SO2) from fertilizer, combustion of fuel in rice production processing, and burning of straw. We assessed endpoint impact by the following methods: (1) We estimated the proportion of rice straw from the annual rice production of the Ministry of Agriculture, Forestry, and Fisheries (MAFF) of Cambodia subjected to open burning in all 25 Cambodian provinces. (2) We calculated air pollutants emissions in terms of kilograms of pollutants per ton of rice produced by following the air pollutant emission inventory (EI) in the EMEP/EEA Guidebook for 2019, using data for fertilizer and fuel combustion that was applied in the field, based on a face-to-face survey of farmers. (3) We analyzed health impact in terms of DALYs using the characterization factor (CF) adapted from the ReCiPe 2016 v1.1 method. The results showed that the total health impact of rice straw production was 13,093.50 DALYs. Our analysis showed that open burning alone contributed 98.55% of the total health impact considered in this study, while fertilizers and combustion contributed only 0.12% and 1.33%, respectively. As the major of the total health impact arose from open burning, a policy of zero open burnings should be an effective way to reduce health impacts. These findings provide information for policymakers on how to alleviate air quality issues caused by the practice of open straw burning by adopting alternative techniques of rice straw management.

Associations of personal exposure to domestic heating and cooking fuel emissions and epidemiological effects on rural residents in the Fenwei Plain, China

Solid fuel combustion for domestic heating in northern China in the wintertime is of great environmental and health concern. This study assesses personal exposure to particulate matter with different aerodynamic diameters and multiple gaseous pollutants from 123 rural residents in Yuncheng, the Fenwei Plain. The subjects are divided into groups based on the unique energy source applied, including biomass, coal, and electricity/no heating activities. The health effects of the exposures are expressed with four urinary biomarkers. The personal exposure levels to three different aerodynamic particle sizes (i.e., PM10, PM2.5, and PM1) of the electricity/no heating group are 5.1 % –12 % lower than those of the coal group. In addition, the exposure levels are 25 %–40 % lower for carbon monoxide (CO) and 10.8 %–20.3 % lower for ozone (O3) in the electricity/no heating group than the other two fuel groups. C-reactive protein (CRP) in the urine of the participants in biomass and coal groups is significantly higher than that in the electricity/no heating group, consistent with the observations on other biomarkers. Increases in 8-hydroxy-2 deoxyguanosine (8-OHdG), interleukin-8 (IL-8), and vascular endothelial growth factor (VEGF) are observed for the exposures to higher concentrations of air pollutants. For instance, PMs and nitrogen dioxide (NO2) show significant impacts on positive correlations with 8-OHdG and IL-8, while O3 positively correlates with CRP. PM1 exhibits higher effects on the biomarkers than the gaseous pollutants, especially on VEGF and IL-8. The study indicates that excessive use of traditional domestic solid fuels could pose severe health effects on rural residents. The promotion of using clean energy is urgently needed in the rural areas of northern China.

Exposure to air pollution and prevalence of metabolic syndrome: A nationwide study in China from 2011 to 2015

BackgroundEvidence concerning the influence of air pollution on metabolic syndrome (MetS) is still limited. We aimed to investigate whether sustained exposure to air pollutants are associated with increased prevalence of MetS and its individual components. MethodsWe conducted a cross-sectional study comprised of 14,097 individuals participated in the first or third survey of the CHARLS. The personal cumulative (3-year averaged) exposure concentrations of nitrogen dioxide (NO2), particulate matter (PM) with a diameter of 1.0 μm or less (PM1), PM with a diameter of 10 μm or less (PM10) and PM with a diameter of 2.5 μm or less (PM2.5) were estimated using a spatiotemporal random forest model at 0.1° × 0.1° spatial resolution based on residential address of each participant provided. We utilized logistic regression models to estimate the associations of the four air pollutants with the prevalence of MetS and its individual components, and performed interaction analyses to evaluate potential effect modifications by gender, health status, age and drinking status. ResultsSustained exposure to air pollutants is associated with increased prevalence of MetS. For every 10 μg/m3 increase in NO2, PM1, PM10 and PM2.5, the adjusted odds ratio (OR) of MetS was 2.276 (95 % CI: 2.148, 2.412), 1.207 (95 % CI: 1.155, 1.263), 1.027 (95 % CI: 1.006, 1.048) and 1.027 (95 % CI: 0.989, 1.066), respectively. For MetS components, we observed significant associations between NO2, PM1, PM10 and central obesity, high blood pressure, elevated fasting glucose and low high-density lipoprotein cholesterol. For example, the adjusted OR of low high-density lipoprotein cholesterol for every 10 μg/m3 increase in NO2 was 1.855 (95 % CI: 1.764, 1.952). We also identified that age could significantly modified the association between NO2 and prevalence of MetS. ConclusionsChinese adults sustained exposure to higher concentrations of air pollutants are associated with increased prevalence of MetS and its components.

Climatology of heatwaves in South America identified through ERA5 reanalysis data

AbstractThe rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019.

Health effects of active mobility and their economic value: Unit benefit factor estimates for Italy

IntroductionThis study aims to quantify, in monetary terms, the health benefits associated with a shift from private cars and motorbikes to active mobility modes in Italy and at providing unit benefit factors to be used in policy making. Modes considered are walking, bikes and e-bikes. MethodsThe quantification is carried out through the development of a mobility-change, ten-year scenario for some Italian cities. The positive effect of increased physical activity levels as well as the negative effects of increased air pollutant intake and road injury risk by active mobility adopters are considered. Health impacts are measured through the change in expected mortality. City-specific mortality rates, demographic structure and the underlying physical activity levels of the population are considered. Changes in mortality rates are translated in monetary units using the value of a life year (VOLY) and the value of a statistical life (VSL). Unit benefit factors expressed in euro/km are derived from total benefits and distance travelled. ResultsAverage benefits associated with increased physical activity outweight damages associated with increased air pollutant intake and road injury risk for all active mobility modes considered. The average overall benefit, however, differs substantially across modes. It is as high as 0.32 euro/km in the case of walking but much lower for bikes (0.06 euro/km) and e-bikes (0.02 euro/km) because of different physical efforts, effort duration and ventilation rates. In cities with high air pollutant background concentrations, e-bike overall benefit can dip to zero. ConclusionsThis is the first work using the VOLY for the monetization of health benefits and including e-bikes among active mobility modes. It also considers the demographic structure and underlying physical activiy levels of the population. The main limitation is that impacts only consider changes in mortality and not in morbidity.

Heterogeneous Impact of Electrification of Road Transport on Premature Deaths from Outdoor Air Pollution: A Macroeconomic Evidence from 29 European Countries

One of the biggest problems associated with vehicles that use internal combustion engines is that they cause elevated levels of pollution in the places they travel through, especially if they cause congestion. However, it is not only the level, but also probably the concentration of gases emitted by internal combustion engines in the places where they move around that is particularly lethal. Can the road transport sector’s electrification mitigate premature deaths from outdoor air pollution? Our main hypothesis is that replacing internal combustion engine vehicles with electrical ones contributes to mitigating people’s exposure to high concentrations of air pollution. To answer the research question, a panel of 29 European countries, from 2010 to 2020, using the method of moments quantile regression and ordinary least squares, was examined. Results support the concept that economic growth, renewable energy consumption, and electric vehicles in all quantiles have a negative impact on premature mortality due to air pollution. These impacts are higher on premature mortality in lower quantiles, but gradually decrease with increasing quantile levels. The results also reveal that methane emissions, in all quantiles except 10th, have a negative effect on premature mortality. Nitrous oxide emissions positively impact premature mortality in all quantiles except the 10th, and this impact increases at high quantiles. Fine particulate matter positively impacts premature mortality in all quantiles, with the same at all levels. The ordinary least squares, used as a robustness check, confirm that economic growth, renewable energy consumption, and methane emissions have reduced impacts on premature mortality due to outdoor air pollution. However, nitrous oxide emissions and fine particulate matter increase premature mortality. These results reinforce the importance of policymakers implementing policies for road electrification.

The Impact of Meteorological Factors and Air Pollutants on Acute Coronary Syndrome.

Purpose of ReviewSeveral studies have found that air pollution and climate change can have an impact on acute coronary syndromes (ACS), the leading cause of death worldwide. We synthesized the latest information about the impact of air pollution and climate change on ACS, the latest data about the pathophysiological mechanisms of meteorological factors and atmospheric pollutants on atherosclerotic disease, and an overall image of air pollution and coronary heart disease in the context of the COVID-19 pandemic.Recent FindingsThe variation of meteorological factors in different seasons increased the risk of ACS. Both the increase and the decrease in apparent temperature were found to be risk factors for ACS admissions. It was also demonstrated that exposure to high concentrations of air pollutants, especially particulate matter, increased cardiovascular morbidity and mortality.SummaryClimate change as well as increased emissions of air pollutants have a major impact on ACS. The industrialization era and the growing population cause a constant increase in air pollution worldwide. Thus, the number of ACS favored by air pollution and the variations in meteorological factors is expected to increase dramatically in the next few years.

Association of air pollution and 1-year clinical outcomes of patients with acute myocardial infarction.

BackgroundExposure to air pollution (AP) is an important environmental risk factor for increased risk of cardiovascular morbidity and triggering acute myocardial infarction (AMI). However, there are limited data regarding the clinical impact of AP on long-term major clinical outcomes of AMI patients. This study aimed to evaluate the clinical effects of ambient AP concentration on short-term and 1-year clinical outcomes of AMI patients.MethodsA total of 46,263 eligible patients were enrolled in the Korea Acute Myocardial Infarction (KAMIR) and KAMIR-National Institutes of Health (NIH) registry from January 2006 to December 2015. We performed Cox proportional hazard regression to assess the risk of all-cause death and any-revascularization according to the annual average concentration of AP during one-year follow-up period.ResultsThe assessment of the annual average of air pollutants before symptom date and all-cause death up to 30 days showed the hazard ratio (HR) of SO2 per 1 part per billion (ppb) increase was 1.084 (95% confidence interval [CI]: 1.016–1.157), and particulate matter with diameter of 10 microns or less (PM10) per 1 μg/m3 increase was 1.011 (95% CI: 1.002–1.021). The results of the 30-day and one-year all-cause death showed a similar trend. For SO2, the HR per 1 ppb increase was 1.084 (95% CI: 1.003–1.172), and the HR of PM10 was 1.021 (95% CI: 1.009–1.033) per 1 μg/m3 increase. We observed that SO2, CO, and PM10 were associated with an increased risk of incidence for any-revascularization up to one-year.ConclusionIn some air pollutants, a higher AP concentration was an environmental risk factor for poor prognosis in AMI patients up to 1 year. AMI patients and high-risk individuals need a strategy to reduce or prevent exposure to high AP concentrations.

The complex relationship of air pollution and neighborhood socioeconomic status and their association with cognitive decline.

Background:Air pollution and neighborhood socioeconomic status (nSES) have been shown to affect cognitive decline in older adults. In previous studies, nSES acts as both a confounder and an effect modifier between air pollution and cognitive decline.Objectives:This study aims to examine the individual and joint effects of air pollution and nSES on cognitive decline on adults 50 years and older in Metro Atlanta, USA.Methods:Perceived memory and cognitive decline was assessed in 11,897 participants aged 50+ years from the Emory Healthy Aging Study (EHAS) using the cognitive function instrument (CFI). Three-year average air pollution concentrations for 12 pollutants and 16 nSES characteristics were matched to participants using census tracts. Individual exposure linear regression and LASSO models explore individual exposure effects. Environmental mixture modeling methods including, self-organizing maps (SOM), Bayesian kernel machine regression (BKMR), and quantile-based G-computation explore joint effects, and effect modification between air pollutants and nSES characteristics on cognitive decline.Results:Participants living in areas with higher air pollution concentrations and lower nSES experienced higher CFI scores (beta: 0.121; 95 % CI: 0.076, 0.167) compared to participants living in areas with low air pollution and high nSES. Additionally, the BKMR model showed a significant overall mixture effect on cognitive decline, suggesting synergy between air pollution and nSES. These joint effects explain protective effects observed in single-pollutant linear regression models, even after adjustment for confounding by nSES (e.g., an IQR increase in CO was associated with a 0.038-point lower (95 % CI: −0.06, −0.01) CFI score).Discussion:Observed protective effects of single air pollutants on cognitive decline can be explained by joint effects and effect modification of air pollutants and nSES. Researchers must consider nSES as an effect modifier if not a co-exposure to better understand the complex relationships between air pollution and nSES in urban settings.

Toxic Air Pollutants and Their Effect on Multiple Sclerosis: A Review Study.

Toxic air pollutants are one of the main factors that have the effect of synergism to increase the incidence of multiple sclerosis (MS). This review aims to investigate the effects of toxic air pollutants on the occurrence of multiple sclerosis (MS). A narrative review of the literature was done from 2000 to 2022 based on various databases such as Google Scholar, Web of Science, Springer, PubMed, and Science Direct. In this study, according to the databases, three hundred and sixty articles were retrieved. Of these, 28 studies were screened after review and 14 full-text articles entered into the analysis process. Finally, 9 articles were selected in this study. According to the finding of this study, toxic air pollutants including polycyclic aromatic hydrocarbons (PAHs), heavy metals (HM), volatile organic compounds (VOCs), particulate matter (PM), and gases are the main agents that cause the development and spread of chronic diseases such as respiratory and cardiovascular diseases, chronic obstructive pulmonary disease (COPD), and multiple sclerosis. The result of this study showed that the main sources of emission of toxic air pollutants include industries, cars, power plants, and the excessive consumption of fossil fuels. In general, the inhalation of high concentration of toxic air pollutants can increase the risk of chronic diseases and multiple sclerosis.

Indoor Air Pollution in Rumuewhera Community in Obio-Akpor Local Government Area of Rivers State, Nigeria

Indoor air pollution arising from the use of biomass fuel for cooking is a serious health issue in Nigeria especially in rural communities. This study investigated the levels of Carbon monoxide (CO), PM2.5 and PM10 released during morning and evening cooking sessions in 17 households in Rumuewhara community in Obio/Akpor LGA, Rivers State Nigeria. This was to ascertain indoor air pollution concentrations in rural households categorized in the terms of fuel type (Firewood, Kerosene and LPG) and kitchen configuration. In the morning cooking session, mean and standard deviation of CO, PM2.5 and PM10 concentration levels from households using LPG (8.78 + 5.20 ppm, 25.5 + 6.65 µg/m3 and 39.38 + 13.28 µg/m3) were observed as lower than those from other households using biomass fuels (36.78 + 19.44 ppm, 270.16 + 159.44 µg/m3 and 419.82 + 247.29 µg/m3 for firewood). The mean concentrations of CO, PM2.5 and PM10 during cooking sessions in firewood kitchens are clearly higher than the standard limits of WHO and Health Canada due to the fuel type, kitchen configuration and ventilation habit. With correlation coefficients, r = -0.537, P=.03; r = -0.583, P=.01 and r = -0.566, p=0.02; there is a statistically significant and strong negative correlation between Relative Humidity vs CO, PM2.5 and PM10 respectively. The use of biomass fuels for household cooking should be discouraged in favour of LPG or kerosene due to the high concentration of indoor air pollutants it generates. To reduce the effects of biomass fuels, well-positioned Chimneys should be incorporated into houses to limit the accumulation of indoor air pollutants in the cooking area.

Effect of Short-Term Exposure to Fine Particulate Matter and Particulate Matter Pollutants on Triggering Acute Myocardial Infarction and Acute Heart Failure

Long-term exposure to high concentrations of air pollution is known to lead to increased cardiovascular disease, but it remains unclear whether short-term exposure increases the incidence of acute myocardial infarction (AMI) and acute heart failure (AHF). A time-stratified case-crossover design was used, including data from the 2-year period (January 1, 2017 to December 31, 2018), from the National Health Insurance Academic Research Database of Taiwan. Air pollution data were obtained from the Air Quality Monitoring Station of the Environmental Protection Agency of the Executive Yuan. A generalized linear model was used for statistical analysis. In areas with a long-term moderate severity of air pollution, a 10 μg/m3 increase in fine particulate matter (PM2.5) and particulate matter (PM10) exposure in a short period of time coincided with an increase in AMI by 6.5% to 6.7% and 0.9% to 1.1%, respectively, and AHF by 6.1% to 6.4% and 0.9% to 1.0%, respectively. A long-term high severity of air pollution (PM2.5 and PM10) coincided with an increase in AMI by 7.9% to 8.8% and 4.4% to 4.9%, respectively, and AHF by 7.6% to 8.4% and 4.3% to 4.8%, respectively. In areas with a long-term moderate or high severity of air pollution, short-term exposure to high concentrations of PM2.5 and PM10 pollution is positively correlated with AMI and AHF.

Indoor Air Quality Forecast in Shared Spaces

The high concentration of air pollutants in indoor environments can have a remarkable adverse impact on health and well-being, cognitive performance and productivity. Indoor air pollutants are especially problematic in naturally ventilated shared spaces such as classrooms and meeting rooms, where human-generated pollutants can rise rapidly. When the inhabitants are exposed to indoor air pollution, recovering from its ramifications takes time and harms their well-being in the long run. In our approach, we seek to predict and prevent such hazardous situations instead of rectifying them after they happen. The prediction and prevention are accomplished through algorithms that can learn from the evolution of air pollutants and other variables to indicate whether or not a high level of pollution is forecast. We present two AI-enabled methods, one providing the forecast for the concentration level of carbon dioxide in the next 5 and 20 minutes with 86% and 92% accuracy. The second algorithm provides predictive indicators about how the CO2 level will evolve during the upcoming session (meeting or a course) before the session starts. We will discuss design implications and present design proposals on how these methods can inform interactive solutions for preventing high concentrations of indoor air pollutants.

Traffic management at intersection to minimize dispersion of air pollution

Abstract One of the consequences of higher mobility is the increase in air pollution. The increase in traffic volume at intersections impacts delays and congestion, thus producing higher air pollution concentrations. This research aimed to identify intersection performance and analyze air quality at existing conditions, then propose traffic management scenarios that increase intersection performance and reduce air pollution at a time. This study was taken on Soekarno-Hatta – M.T. Haryono – D.I. Panjaitan intersection, in Malang. The intersection performance was at the worst level of service (LOS F) for each approach from traffic analysis using the Indonesian Highway Capacity Manual. At this condition, the prediction of air pollutant parameters was out of standard level. The CO concentrate was lower than the standard level, while NO was higher than the standard level. Traffic management was proposed by controlling the movements at the intersection approaches and the number of phases at the intersection. The third alternative can improve intersection performance from F to C and decrease air pollution. The concentration reductions are about ±37.4% for NOx and ±61.3% for CO from the road median to the end of the road.

Toward Cleaner Transport Alternatives: Reduction in Exposure to Air Pollutants in a Mass Public Transport.

Commuters are often exposed to higher concentrations of air pollutants due to its proximity to mobile sources. Despite recent trends in urban transport toward zero- and low-tailpipe emission alternatives, the assessments of the impact of these transformations on commuter exposure are limited by the low frequency of such studies. In this work, we use a unique data set of personal exposure concentration measurements collected over the span of 5 years to analyze changes due to the introduction of a new fleet for Bogotá's Bus Rapid Transit System. In that system, over a thousand Euro-II and -III diesel-powered buses were replaced with Euro-VI compressed natural gas and filter-equipped Euro-V diesel buses. We measured personal exposure concentrations of equivalent black carbon (eBC), fine particulate (PM2.5), and ultra fine particles (UFP) during and after the retirement of old buses and the introduction of new ones. Observations collected prior to the fleet renewal were used as baseline and later compared to data collected over two follow-up campaigns in 2019 and 2020. Significant reductions in the concentration of PM2.5 and eBC were observed during the 2019 campaign, with a 48% decrease for mean in-bus eBC (89.9 to 46.4 μg m-3) and PM2.5 (180.7 to 95.4 μg m-3) concentrations. Further reductions were observed during the 2020 follow-up, when the fleet renovation was completed, with mean in-bus eBC decreasing to 17.7 μg m-3 and PM2.5 to 42.3 μg m-3. These observations imply nearly a 5-fold reduction in eBC exposure and a 4-fold decrease in PM2.5. There was a much smaller reduction of in-bus UFP concentration between 2019 and 2020, indicating a persistent presence of high particle number concentrations in the near-road environment despite the fleet renovation process. In-bus UFP concentrations ranged between 65 000 and 104 500 cm-3 during the follow-up campaigns. The results in this work illustrate the immediate benefits of reducing personal exposure through the adoption of vehicles with more stringent emission standards.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

Effect of household air pollutants on the composition of exhaled breath characterized by solid-phase microextraction and needle-trap devices.

Exposure to household air pollutants is becoming a serious environmental health risk. Various methods can be applied to assess humans' exposure status to indoor pollutants, with breath monitoring being among the best options. Breath sampling is fast and non-invasive, and contains compounds that can be used as markers for evaluating exposure length and estimating internal concentrations of pollutants. However, the distribution of compounds between gas and droplets in breath samples represents one of the key challenges associated with this analytical method. In this work, a needle-trap device (NTD) was prepared by packing the needle with a porous filter, divinyl benzene, and Carboxen to enable the exhaustive capture of both droplet-bound and gaseous components. Furthermore, fiber-based solid-phase microextraction (SPME) was also applied to extract compounds from only the gas phase to distinguish this portion of analytes from the total concentration in the sample. Dynamic, real-time breath sampling was enabled via a new sampling tube equipped with 2 one-way valves, which was specially designed for this work. Both methods provided satisfactory reproducibility, repeatability, and sensitivity, with detection limits as low as 0.05ngmL-1. To investigate the real-world applicability of the proposed devices, breath samples were obtained from volunteers who had been exposed to candle and incense smoke and aerosol sprays, or had smoked cannabis. The results revealed the high concentration of organic air pollutants in inhaled air (maximum of 215ngmL-1) and exhaled breath (maximum of 14.4ngmL-1) and a correlation between the components in inhaled air and exhaled breath. Significantly, the findings further revealed that the developed NTD has enhanced breath-sample determinations, especially for polar compounds, which tend to remain trapped in breath droplets.