Air Monitoring Data Research Articles

Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT+CRF Model

(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points’ address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale; furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.

Abstract 1437: Ambient air pollution and risk of prostate cancer: The multiethnic cohort study

Abstract Previous epidemiological evidence on air pollution and prostate cancer risk is limited, with few studies examining the time varying effects of air-pollution, and primarily among whites in the US and Canada. To further our understanding on this topic, we assessed ambient air pollutants in relation to prostate cancer incidence in a large multi-ethnic population. We included 33,830 men aged 45 years or older at baseline (1993-1996) who resided in Southern California at enrollment in the Multiethnic Cohort (24.5% African American, 15.1% Japanese American, 48.4% Latino, 0.2% Native Hawaiian, and 11.8% White). Until 2016, with a median of 20 years of follow-up, 4,540 prostate cancer cases were identified by linkage to the California SEER cancer registry. Monthly exposures to particulate matter with aerodynamic diameter ≤ 10 μm (PM10) and nitrogen oxides (NOx) per participant were estimated by Kriging interpolation based on air-monitoring data and residential addresses. We used Cox proportional hazards models to estimate the association between time-varying air-pollutant levels and prostate cancer risk, using age in months as the time metric. We adjusted for neighborhood socioeconomic status (nSES) at baseline and current nSES at event, education, race/ethnicity, smoking pack years, BMI, diabetes status, asthma history, and family history of prostate cancer, using age at cohort entry as the strata variable. The average NOx and PM10 over the study period was 73.1 parts per billion (ppb) and 38.1 μg/m3 in prostate cancer cases, respectively, and 60.9 ppb and 35.5 μg/m3 in men without prostate cancer, respectively. Across racial/ethnic groups, African Americans had the highest average NOx levels (74.3ppb) and Latinos had the highest average PM10 levels (39.0 μg/m3) over the study period. In the overall population, NOx per 50 ppb was statistically significantly associated with an increased risk of prostate cancer [hazard ratio (HR)=1.12, 95% confidence interval (CI): 1.01-1.25] and PM10 per 10 μg/m3 was associated with a suggestive increased risk (HR=1.07, 95%CI: 0.99-1.16). In analysis by race/ethnicity, statistically significant positive associations with NOx (HR=1.25, 95%CI: 1.07-1.46) and PM10 (HR=1.21, 95%CI: 1.07-1.38) were detected among African American, but not consistently in other racial/ethnic groups. In a subset of 21,537 men with no residential moves over the follow-up period, we observed positive associations for both NOx and PM10 similar to those among all men. In conclusion, our results suggest that both NOx and PM10 are associated with an increased risk of prostate cancer. Analyses of other air pollutants in relation to prostate cancer risk and disease aggressiveness are ongoing. Citation Format: Anqi Wang, Chiu-chen Tseng, Heather Rose, Iona Cheng, Anna H. Wu, Christopher A. Haiman. Ambient air pollution and risk of prostate cancer: The multiethnic cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1437.

An Exploratory Investigation of Government Air Monitoring Data after Hurricane Harvey.

Southeast Texas is home to some of the largest refineries in the United States. During Hurricane Harvey, emergency shutdowns took place. In this exploratory investigation, we examine how government air monitors performed in measuring air quality in Beaumont, Texas during and in the months following Hurricane Harvey. Texas Commission on Environmental Quality (TCEQ) data from two active air monitors in Beaumont, Texas were analyzed during the year 2017–2018. Concentrations of sulfur dioxide (SO2), nitric oxide (NO), oxides of nitrogen (NOx), ozone, benzene, and hydrogen sulfide (H2S) were investigated. The number of hours and days no data were reported by air monitors were also investigated. Yearly maximum values (MAX, all in parts per billion (ppb)) in 2017 for SO2, NO, and NOx (53.7, 113.4, 134, respectively) and their respective standard deviations (SD: 1.3, 3.4, and 14) were higher as compared to 2018 (MAX, all in ppb and (SD) = 40.9, (1.4); 103.9, (3.3); 123.8, (14), respectively). The data capture rate for these chemicals were between 88 and 97% in both years. During the months following Hurricane Harvey (August–December 2017) there was an increase in most maximum values. The yearly averages for H2S were 0.68 ppb (SD 1.02) in 2017 and 0.53 ppb (SD 1.07) in 2018. Missing days were observed for both the H2S and NOx air monitors, with the highest number observed in 2017 (213 missing days) for the air monitor measuring H2S. We identified that residents of Beaumont, Texas are exposed daily to low-level concentrations of air pollutants. H2S is released each day at a level high enough to be smelled. Data capture rates for air monitors are not always above 90%. Improved air quality data and disaster preparations are needed in Beaumont, Texas.

Industrial emissions effect into atmospheric air quality: mathematical modeling.

This paper presents the results of modeling the distribution process of industrial emission components at specified distances from the emission source along the normal. The model uses a system of differential diffusion equations to compute the concentration profiles of aerosols, industrial gases, and fine particles in the atmosphere. In order to investigate the regularity of the emitter propagation into the atmosphere, a theory of impurity dispersion was developed. The model is constrained by the effect of particle interactions. The partial derivative equations are presented to calculate the concentrations of aerosols and fine particles under the turbulent airflow in the atmosphere, dispersion of inert impurities, and distribution of chemically active compounds. The adequacy of the mathematical model for a series of theoretical calculations was checked by contrasting the data of the atmospheric air monitoring for the cities of Almaty, Ust-Kamenogorsk, Pavlodar, Atyrau, Krasnodar, Chelyabinsk, Beijing, and Shanghai. Air monitoring data included PM10, SO2, and NO2 levels. The mathematical model solutions for the relative values of the emitter concentration in the direction along the normal of the pollution source at the surface were obtained. Graphical interpretation of the calculation results over the 0…200m distance for time intervals ranging from 3 to 600min was provided. According to the multiple factor cluster analysis, the critical values of SO2 concentrations in Atyrau exceeded MPC in 26.2% of cases. The level of NO2 for Shanghai was 15.6%, and those for PM10 concentrations in Almaty and Atyrau amounted to 16.4%. A comparison of theoretical values and results obtained from official sources showed arithmetic mean of 49.4mg/m3 and maximum value of 823.0mg/m3. Standard deviation comprised 48.9mg/m3. Results were considered statistically significant at p≤0.005. The mathematical model developed in this study can be used to predict the status of atmospheric air.

A critical evaluation of air quality index models (1960-2021).

The formulation of an adequate and practical Atmospheric Air Quality Management Plan at different spatial scales at local (micro), city (medium), national (macro)), and temporal (short and long term) is an indispensable solution to prevent the public from air pollution health risk. The air quality monitoring system provides regulatory agencies a comprehensive data of current air contaminants in a particularlocation. Then, air monitoring data of pollutants is processed into a dimensionless unit called the "Air Quality Index" (AQI); it serves as an information medium for the people to know the air quality health of their location and takes preventative steps accordingly (public participation). Thus, the AQI is a beneficial tool for the public, stakeholders, and regulators to understand the current state of air quality. AQI across the globe considers the number of pollutants (most of the developed countries and some developing countries considers PM2.5 to measure the overall status of air quality being monitored), averaging time for which pollutants are measured, calculation method to compute air quality indices for each pollutant, calculation mode to aggregate the overall index, scale of an index, categories, colour coding scheme, and related descriptive terms of the pollutants. This article presents rationalized and extensive reviews of various Air Quality Index (AQI) models utilized worldwide from 1960 to 2021, comparing them based on several parameters such as types and number of pollutants (criteria or hazardous air pollutants), averaging time (long-term or short-term), calculation methods (linear or nonlinear), calculation modes [single-pollutant (maximum value) or multi-pollutants (combined effect)]. By analysing the strengths and flaws of all the AQI models developed so far, it is recommended to develop a more reliable, extensible, and comparable AQI model to be employed as an executive tool for designing strategic pollution abatement programs to preserve public health.

Testing HYSPLIT Plume Dispersion Model Performance Using Regional Hydrocarbon Monitoring Data during a Gas Well Blowout

A gas well blowout in south central Texas in November 2019 that lasted for 20 days provided a unique opportunity to test the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model’s plume dispersion against hydrocarbon air monitoring data at two nearby state monitoring stations. We estimated daily blowout hydrocarbon emission rates from satellite measurement-based results on methane emissions in conjunction with previously reported composition data of the local hydrocarbon resource. Using highly elevated hydrocarbon mixing ratios observed during several days at the two downwind monitoring stations, we calculated excess abundances above expected local background mixing ratios. Subsequent comparisons to HYSPLIT plume dispersion model outputs, generated using High-Resolution Rapid Refresh (HRRR) or North American Mesoscale (NAM) forecast meteorological input data, showed that the model generally reproduces both the timing and magnitude of the plume in various meteorological conditions. Absolute hydrocarbon mixing ratios could typically be reproduced within a factor of two. However, when lower emission rate estimates provided by the company in charge of the well were used, downwind hydrocarbon observations could not be reproduced. Overall, our results suggest that HYSPLIT, in combination with high-resolution meteorological input data, is a useful tool to accurately forecast chemical plume dispersion and potential human exposure in disaster situations.

Monitoring-based assessment of environmental pollution in regions of the Russian Federation

Abstract The article assesses the environmental pollution level in urban areas of the Russian Federation regions. Environmental monitoring data were used as data sources. For each Russian region the environmental pollution level was considered as the sum of pollution indices for the basic environmental media: air, surface water and soil. It allowed ranking regions and grouped them into categories (from extremely high polluted to extremely low ones). The air pollution monitoring network in cities and industrial centres does not cover all regions of the country, leading to undetermined air pollution level in 12 of 85 regions and thus to underestimated environmental pollution level there. The paper proposes to use the monitoring network data of the snow cover chemistry for the air pollution assessment in problematic regions and more accurate calculation of the environmental pollution level in them. Based on the air monitoring data for 2018 we revealed regions with high and extremely high levels of air pollution, their list was added after analyzing the snow cover chemistry data. As a result, the total assessment of environmental pollution was recalculated upward.

Quantification of temperature dependence of NO emissions from road traffic in Norway using air quality modelling and monitoring data

Emissions of nitrogen oxides (NO x ) from road traffic are dependent on a range of factors including vehicle type, speed, driving patterns and engine temperature. Recently a number of studies have indicated that ambient air temperature plays an important role in vehicle NO x emissions, mainly due to various technical challenges of diesel vehicles that occur at low ambient temperatures. This study aims to derive a correction formula to account for this temperature dependence when calculating emissions from road traffic in Norway. Measured NO x concentrations in the period 2016–2019 at 46 sites dominated by road traffic sources are compared to the NO x concentrations calculated with the chemistry-transport modelling system EMEP/uEMEP at the same sites. The model has good road traffic volume input data, but no temperature dependence in the emission factors. A clear temperature dependence in the observed-to-modelled ratio of NO x concentration is found. The ratio increases from 1.09 at high temperatures to 2.9 at low temperatures. The increase occurs gradually in the temperature range from -13 °C to +14 °C. Assuming this temperature dependence in the bias is due to the road traffic emissions, a correction formula for these emissions is derived. The correction factor is 1 at high temperatures and 3.28 at low temperatures, with a linear increase in the range from +12.4 °C to -12.9 °C. Thus, our results suggest that road traffic emissions should be 3.3 times higher at temperatures below -13 °C than at high temperatures, and 2.7 times higher at -7 °C. The temperature range and magnitude of this temperature dependence are consistent with the existing literature on emission measurement experiments performed on various models of diesel vehicles. The derived temperature dependence can be used to correct road traffic emissions. However, the parameter values in the correction are dependent on the vehicle fleet composition and are applicable only for the current Norwegian vehicle fleet. • Road traffic NOx emission increase in cold weather found from dispersion model bias. • Emissions estimated to be 2.7 (3.3) times higher at -7 °C (-13 °C) than at >14 °C. • Results agree with literature on emission measurements of individual vehicles.

Characterization of roadside air pollutants: An artery road of Lanzhou city in northwest China

Due to the lack of air and traffic monitoring data in China, this study employed a remote sensing (RS) system and air quality monitoring sensors to investigate the air pollutant concentrations, traffic profiles and meteorological data in order to characterize roadside pollutant concentrations and identify their influence factors for a major road in Lanzhou. The results suggested that the temporal variations of concentrations of SO2, NO2, PM2.5, PM10 and CO peaked at 5:00-7:00 while O3 peaked at 17:00. The concentrations of roadside air pollutants were lower on weekdays than weekends except O3. The concentrations of PM2.5 and PM10 declined with the height of the monitoring sites increased, peaking at 2-meter height whereas SO2, NO2, CO, and O3 reached their highest concentrations at 4-meter height. The concentrations of roadside NO2 and CO show positive correlations with the volume of heavy-duty trucks (HDTs) and light-duty passenger vehicles (LDPVs), respectively. This study provided scientific support to the establishment of roadside air quality monitoring systems and the assessment of the environmental and health impact from vehicle emissions.

Quantitative assessment of renovation of “Petmol” plant in Saint-Petersburg

The standardized harmful effect of industrial production on buildings is one of the reasons for the plant withdrawal to the outskirts. The paper examines a particular case of renovation of the urban industrial area for residential commercial development. The energy consumption, environmental pollution, demographic burden, drains. and other indicators are analyzed. The calculation results show that restrained urban conditions can surpass the industrial in terms of the negative impact on the environment, and therefore renovation leads to an increase in resource consumption and deterioration of the environmental situation. Air monitoring data are used to confirm the correctness of the calculations.

Towards Multi-Scale Space-Time Characteristics of Air Quality and Population Exposure Risk

In order to formulate policies to control regional air pollution and promote sustainable human–land system development, it is crucial to study the space–time distribution of air pollution and the population exposure risk. Existing studies are limited to individual fine particulate pollutants, which does not fully reflect the comprehensiveness of air quality. In addition, the spatiotemporal distribution of air quality and population exposure risk at different scales need to be further quantified. In this study, we used air monitoring station data and population spatial distribution data to analyze the spatiotemporal characteristics of air quality, including seasonal variations, variations before and during heating periods, and the occurrence frequency of priority pollutants in the traditional industrial areas of Northeast China in 2015. The population exposure–air pollution risk (PE-APR) model was used to calculate the population exposure risk at different spatial scales. The results suggest that GIS methods and air monitoring data help to establish a comprehensive air quality analysis framework, revealing spring–summer differentiation and the change trend of air quality with latitude. There are significant clustering features of air quality. A grid-scale population exposure–air pollution risk map is not restricted by administrative boundaries, which helps to discover high-risk areas of the main regional economic corridors and differences between inner cities and suburbs. This study provides a reference for understanding the space–time evolution of regional air pollution and formulating coordinated cross-regional air pollution strategies.

Heavy Metals in Air Emission and Assessment of Human Toxicity Footprint

Objective : The purpose of this study is to propose the concept of the human toxicity (human cancer and non-cancer) footprint (µg 1,4 DCB (Dichlorobenzene) eqv./m3) using heavy metal concentration data in the air in Provincial and Metropolitan City governments. In addition, the final goal is to assess and compare the human carcinogenic toxicity footprint in Provincial and Metropolitan City Governments from 1991 to 2019.Method : To calculate the human carcinogenic toxicity footprint using heavy metal concentration in Provincial and Metropolitan City Governments, the ReCiPe 2016 life cycle impact assessment method in life cycle assessment (LCA) was used. For the human carcinogenic toxicity footprint calculation, the heavy metal concentration data in Provincial and Metropolitan City Governments are multiplied by the characterization factors of each heavy metal such as Pb, Cd, Cr, Ni, and As (Cu, Mn, Fe, Al, Ca, and Mg are not included in the air monitoring data) data (µg/m3). The unit of human carcinogenic toxicity footprint is shown in µg 1,4 DCB eqv. value.Results and Discussion : The results show that the heavy metal concentrations in Provincial and Metropolitan City governments were decreased significantly from 1991 to 2019. In the case of Pb, Incheon was decreased by about 2,124% (from 0.427 µg/m3 to 0.0192 µg/m3), and Busan was decreased by about 1,250% (from 0.2471 µg/m3 to 0.0183 µg/m3). In addition, in Cd’s case, Gwangju decreased by about 1,550% (from 0.0033 µg/m3 to 0.0002 µg/m3), and Incheon was decreased by about 1,071% (from 0.0082 µg/m3 to 0.0007 µg/m3). The results of human carcinogenic toxicity footprint in 1991 (early stage), 2005 (middle-stage), and 2019 (current) show that the average of human carcinogenic toxicity footprint was 8,478 µg 1,4 DCB eqv. in 1991, 5,545 µg 1,4 DCB eqv. in 2005 (about 53% decreased from 1991), and 1,997 µg 1,4 DCB eqv. in 2019 (about 178% decreased from 2005). In 2019, the human carcinogenic toxicity footprint results showed that Gyeongbuk had the highest value of 5,184 µg 1,4 DCB eqv. followed by Busan 3,929 µg 1,4 DCB eqv., Gyeongi 3,305 µg 1,4 DCB eqv., Seoul 2,184 µg 1,4 DCB eqv., Gwangwon 2,011 µg 1,4 DCB eqv. One of the reasons for the highest human carcinogenic toxicity footprint is the concentration of Cr and As, which have very high characterization factors.Conclusions : In this study, the human carcinogenic toxicity footprint using a heavy metal concentration in Provincial and Metropolitan City governments was proposed and calculated. Even though the heavy metal concentrations were decreasing, the number of measuring stations should be increased in each Provincial and Metropolitan City Government to improve the data quality and reduce the human carcinogenic toxicity impact by heavy metals.

Cumulative risk assessment for public schools in a predominantly poor, minority and heavily industrialized area of Louisiana

BACKGROUND AND AIM: This research aimed to characterize environmental conditions of public schools in St. John the Baptist Parish, Louisiana, US. METHODS: Estimates of health risks, both cancer and non-cancer, were derived for children at all school locations in the parish, from: 1) inhalation of chloroprene for all relevant schools ( 2.0 miles from an industrial source), and 2) inhalation of all facility emissions for all schools in the parish (cumulative risks). Risks of concern were identified based on EPA recommended guidelines (1 cancer per 10,000 people exposed). Other data such as biomonitoring data, health statistics, and state investigations were evaluated to determine if community exposures and expected adverse health outcomes are or may be occurring. RESULTS:Health statistics suggest that adverse health outcomes associated with industry emissions may be occurring. Air monitoring data, risk estimates and biomonitoring data suggest that chloroprene air concentrations exceed health-based guidelines. In addition, health symptoms impacting students at one site have been associated with ethylene oxide. Community asthma-, and cancer-specific health statistics support a need for further investigation of potential acute and chronic impacts. CONCLUSIONS:Given the number of chemicals released, their emitted volumes, and variability in the mixtures residents are exposed to, it is difficult to quantify risk without some degree of error. And while no clear relationship between health and exposure measures can be determined, proactive measures should be taken to reduce children’s exposures. Gaps in our understanding of long-term health impacts of early-life exposures to chloroprene and different chemical mixtures, a history of industrial accidents in the area, and audit determinations documenting lax state regulatory oversight, enforcement, and risk communication, should propel school administrators to move children away from high-risk locations, as potential lifelong impacts on developing children could outweigh any economic benefits these emissions pose for industry. KEYWORDS: Cumulative risk assessment, chloroprene, ethylene oxide, schools, industry

Influences of Windblown Particulate Matter on Children’s Respiratory Health Residing Near the Salton Sea, California

BACKGROUND AND AIM: The production of wind-blown dust and dust storms is expected to accelerate as climate change drives land use changes and diminishes water resources. For instance, in the rural U.S./Mexico border region of the Imperial Valley, sources of wind-blown particulate matter (PM) are growing due to anthropogenic activities and the shrinking of the Salton Sea, a land-locked saline “sea” situated in the Southern California region. Residents of this environmental justice community have expressed growing concern about these environmental changes, including the health implications of worsening air quality. METHODS: To assess the impacts of PM on children’s respiratory health in a structurally marginalized community, we administered a survey to parents of elementary school children (n=456). We estimated children’s exposure to PM2.5 and PM10 using air monitoring data from governmental regional monitors based on residence at time of survey. RESULTS:22% of children reported wheeze in the past 12 months. Individual annual exposure estimates for children were on average 7.55μg/m3 (SD: 1.9) and 42.44μg/m3 (SD: 6.75) for PM2.5 and PM10, respectively. In models adjusted for age, sex, parental education, and survey language, a 1SD increase in PM10 was associated with an increased risk of wheezing in the last 12 months (OR: 1.35, 95% CI: 0.9-1.8, P=0.06). Among asthmatics, a 1SD increase in PM2.5 or PM10 was associated with an increased risk of wheezing (PM2.5 OR: 2.2, 95% CI: 1.4-3.4, P=0.0006; PM10 OR: 1.9, 95% CI: 0.9-4.0, P=0.07), and any asthma medication usage (PM2.5 OR: 1.9, 95% CI: 1.2-2.9, P=0.005; PM10 OR: 2.4, 95% CI: 1.1-4.9, P=0.01). CONCLUSIONS:These results suggest that PM2.5 and PM10 air pollution may influence children’s risk of wheezing in this rural border region. Future work aims to identify additional environmental and modifiable risk factors to facilitate implementation of targeted intervention programs to improve health. KEYWORDS: Environmental Justice, rural health, border health, respiratory health

Maternal PM10 Exposure Increases Risk for Spina Bifida: A Population-Based Case-Control Study

Limited studies have focused on the impact of ambient air pollution on spina bifida. A population-based case-control study was conducted in Liaoning Province, China to assess the associations between maternal PM10 exposures in various exposure windows and spina bifida risk. Data on spina bifida cases born between 2010 and 2015 were available from the Maternal and Child Health Certificate Registry of Liaoning Province. Controls were a random sample of healthy livebirths without any birth defects delivered in the selected five cities during 2010–2015. Ambient air monitoring data for PM10 were obtained from 75 monitoring stations in Liaoning Province. The multivariable logistic regression models were established to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI). We further performed sensitivity analyses by using three propensity score methods. A total of 749 spina bifida cases and 7,950 controls were included. After adjusting for potential confounders, spina bifida was associated with a 10 μg/m3 increment in PM10 during the first trimester of pregnancy (adjusted OR = 1.06, 95% CI: 1.00–1.12) and the 3 months before pregnancy (adjusted OR = 1.12, 95% CI: 1.06–1.19). The adjusted ORs in the final model for the highest vs. the lowest quartile were 1.51 (95% CI: 1.04–2.19) for PM10 during the first trimester of pregnancy and 2.01 (95% CI: 1.43–2.81) for PM10 during the 3 months before pregnancy. Positive associations were found between PM10 exposures during the single month exposure windows and spina bifida. Sensitivity analyses based on two propensity score methods largely reported similar positive associations. Our findings support the evidence that maternal PM10 exposure increases the risk of spina bifida in offspring. Further, validation with a prospective design and a more accurate exposure assessment is warranted.

A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda

The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R2) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 μg/m3 (IQR: 38.11, 62.84 μg/m3)—well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R2 values ranged between 7.65 μg/m3 – 16.85 μg/m3 and 0.24–0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R2 = 0.84; RMSE = 7.65 μg/m3). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.

Exposure to atmospheric metals using moss bioindicators and neonatal health outcomes in Portland, Oregon

Studying the impacts of prenatal atmospheric heavy-metal exposure is challenging, because biological exposure monitoring does not distinguish between specific sources, and high-resolution air monitoring data is lacking for heavy metals. Bioindicators - animal or plant species that can capture environmental quality - are a low-cost tool for evaluating exposure to atmospheric heavy-metal pollution that have received little attention in the public-health literature. We obtained birth records for Portland, Oregon live births (2008–2014) and modeled metal concentrations derived from 346 samples of moss bioindicators collected in 2013. Exposure estimates were assigned using mother's residential address at birth for six metals with known toxic and estrogenic effects (arsenic, cadmium, chromium, cobalt, nickel, lead). Associations were evaluated for continuous (cts) and quartile-based (Q) metal estimates and three birth outcomes (preterm birth (PTB; <37 weeks)), very PTB (vPTB; <32 weeks), small for gestational age (SGA; 10th percentile of weight by age and sex)) using logistic regression models with adjustment for demographic characteristics, and stratified by maternal race. Chromium and cobalt were associated with increased odds of vPTB (chromium - odds ratio (OR)cts = 1.09, 95% CI: 1.00, 1.17; cobalt - ORQ4vsQ1 = 1.33, 95% CI: 1.03, 1.71). Cobalt, chromium and cadmium were significantly associated with odds of SGA, although the direction of association differed by metal (cobalt - ORcts = 1.04, 95% CI: 1.01, 1.07; chromium - ORQ3vsQ1 = 0.91, 95% CI: 0.83, 0.99; cadmium - ORcts = 0.96, 95% CI: 0.93, 1.00). In stratified analyses, odds of SGA were significantly different among non-white mothers compared to white mothers with exposure to chromium, cobalt, lead and nickel. This novel application of a moss-based exposure metric found that exposure to some atmospheric metals is associated with adverse birth outcomes. These findings are consistent with previous literature and suggest that moss bioindicators are a useful complement to traditional exposure-assessment methods.

Occupational Exposure to Diisocyanates in the European Union.

ObjectivesDiisocyanates are a chemical group that are widely used at workplaces in many sectors. They are also potent skin- and respiratory sensitizers. Exposure to diisocyanates is a main cause of occupational asthma in the European Union. To reduce occupational exposure to diisocyanates and consequently the cases of diisocyanate-induced asthma, a restriction on diisocyanates was recently adopted under the REACH Regulation in the European Union.MethodsA comprehensive evaluation of the data on occupational exposure to the most important diisocyanates at workplaces was made and is reported here. The diisocyanates considered are methylene diphenyl diisocyanate (MDI), toluene diisocyanate (TDI), and hexamethylene diisocyanate (HDI), accounting for more than 95% of the market volume in the EU. The exposure assessment is based on data from Chemical Safety Reports (CSRs) of REACH Registration Dossiers, workplace air monitoring data from Germany, from the UK Health and Safety Executive (HSE), and literature data relevant for the EU, and the USA.ResultsOccupational exposure to diisocyanates is particularly relevant in: (i) C.A.S.E. applications (Coatings, Adhesives, Sealants, Elastomers), (ii) production of polyurethanes (PUs) (e.g. slab-stock foam), (iii) handling of partly uncured PU products (e.g. cutting, demoulding, spray application of foam), and (iv) when diisocyanates/PUs are heated (e.g. hot lamination, foundry applications/casting forms). Ranking of the reported data on inhalation to diisocyanate exposure at workplaces (maximum values) leads to following order: (i) HDI and its oligomers in coatings, (ii) MDI in spray foam applications, (iii) TDI in manufacture of foam, (iv) TDI in manufacture of PUs and PU composite materials, (v) TDI in adhesives, (vi) MDI in adhesives, (vii) MDI in manufacture of PUs and PU composite materials, (viii) TDI in coatings, (ix) MDI in manufacture of foam, and (x) HDI in adhesives.

Impact of a pollution breach at a coke oven factory on asthma control in nearby vulnerable adults

Previous studies have related sulfur dioxide (SO2) exposure to asthma exacerbations. We utilized the University of Pittsburgh Asthma Institute registry to study associations of asthma exacerbations between 2 geographically distinct populations of adults with asthma. Our objective was to examine whether asthma symptoms worsened following a significant fire event that destroyed pollution control equipment at the largest coke works in the United States. Two groups of patients with asthma, namely, those residing within 10 miles of the coke works fire (the proximal group [n= 39]) and those residing beyond that range (the control group [n= 44]), were geocoded by residential address. Concentrations of ambient air SO2 were generated by using local University of Pittsburgh Asthma Institute registry air monitoring data. Factory emissions were also evaluated. Data from a patient historical acute exposure survey and in-person follow-up data were evaluated. Inferential statistics were used to compare the groups. In the immediate postfire period (6-8 weeks), the level of emissions of SO2 from the factory emissions increased to 25 times more than the typical level. Following the pollution control breach, the proximal cohort self-reported an increase in medication use (risk ratio= 1.76; 95% CI= 1.1-2.8; P< .01) and more exacerbations. In a small subset of the follow-up cohort of those who completed the acute exposure survey only, asthma control metrics improved. Real-world exposure to a marked increase in ambient levels of SO2 from a pollution control breach was associated with worsened asthma control in patients proximal to the event, with the worsened control improving following repair of the controls. Improved spatial resolution of air pollutant measurements would enable better examination of exposures and subsequent health impacts.

Childhood traffic-related air pollution and adverse changes in subclinical atherosclerosis measures from childhood to adulthood

BackgroundChronic exposure to air pollutants is associated with increased risk of cardiovascular disease (CVD) among adults. However, little is known about how air pollution may affect the development of subclinical atherosclerosis in younger populations. Carotid artery intima-media thickness (CIMT) is a measure of subclinical atherosclerosis that provides insight into early CVD pathogenesis.MethodsIn a pilot study of 70 participants from the Southern California Children’s Health Study, we investigated CIMT progression from childhood to adulthood. Using carotid artery ultrasound images obtained at age 10 and follow-up images at age 21–22, we examined associations between childhood ambient and traffic-related air pollutants with changes in CIMT over time and attained adult CIMT using linear mixed-effects models adjusted for potential confounders.Average residential childhood exposures (i.e., birth to time of measurement at 10–11 years) were assigned for regional, ambient pollutants (ozone, nitrogen dioxide, particulate matter, interpolated from regulatory air monitoring data) and traffic-related nitrogen oxides (NOx) by road class (modeled using the CALINE4 line source dispersion model). Traffic density was calculated within a 300-m residential buffer.ResultsFor each 1 standard deviation (SD) increase in childhood traffic-related total NOx exposure, we observed greater yearly rate of change in CIMT from childhood to adulthood (β: 2.17 μm/yr, 95% CI: 0.78–3.56). Increases in annual rate of CIMT change from childhood to adulthood also were observed with freeway NOx exposure (β: 2.24 μm/yr, 95% CI: 0.84–3.63) and traffic density (β: 2.11 μm/yr, 95% CI: 0.79–3.43). Traffic exposures were also related to increases in attained CIMT in early adulthood. No associations of CIMT change or attained level were observed with ambient pollutants.ConclusionsOverall, we observed adverse changes in CIMT over time in relation to childhood traffic-related NOx exposure and traffic density in our study population. While these results must be cautiously interpreted given the limited sample size, the observed associations of traffic measures with CIMT suggest a need for future studies to more fully explore this relationship.