Air Pollution Epidemiological Studies Research Articles

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia.

Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

Determination of uptake rates of volatile organic compounds by 24-hour passive sampling and their application in enclosed ship cabin environments

Effective and reliable uptake rates are crucial for applying passive samplers in monitoring gaseous volatile organic compounds (VOCs). However, there is currently a lack of data regarding the 24-h passive uptake rates of VOCs for workplace and environmental monitoring. This study aims to address this gap by utilizing a Tenax TA-based sampling tube to investigate the 24-h uptake rates of 25 kinds of VOCs in enclosed ship cabin environments. The results reveal that the uptake rate of VOCs ranges from 0.32 to 0.44 mL/min. A relationship between diffusive uptake rates and the boiling point of the compounds is proposed. The variation in uptake rate under different sampling durations was examined. The difference in uptake rate between 24 h and 8 h was smaller than that between 24 h and 6 days, with an average difference of 20%. In addition, the indoor uptake rate obtained in this study was compared with the outdoor uptake rates in the literature, and results showed that the outdoor uptake rate was generally higher than the indoor uptake rate, but the difference decreased when the sampling duration was longer. Field measurement was conducted by utilizing the measured uptake rates to characterize VOC pollution in both living and engine cabins in actual ships. This study contributes to facilitating the evaluation of environmental daily average concentrations in enclosed ship cabins, which will be helpful for assessing the effectiveness of air pollution control systems or epidemiological studies in ships.

Associations between short-term exposure to PM2.5, NO2 and O3 pollution and kidney-related conditions and the role of temperature-adjustment specification: A case-crossover study in New York state

Epidemiologic evidence on the relationship between air pollution and kidney disease remains inconclusive. We evaluated associations between short-term exposure to PM2.5, NO2 and O3 and unplanned hospital visits for seven kidney-related conditions (acute kidney failure [AKF], urolithiasis, glomerular diseases [GD], renal tubulo-interstitial diseases, chronic kidney disease, dysnatremia, and volume depletion; n = 1,209,934) in New York State (2007–2016). We applied a case-crossover design with conditional logistic regression, controlling for temperature, dew point temperature, wind speed, and solar radiation. We used a three-pollutant model at lag 0–5 days of exposure as our main model. We also assessed the influence of model adjustment using different specifications of temperature by comparing seven temperature metrics (e.g., dry-bulb temperature, heat index) and five intraday temperature measures (e.g., daily mean, daily minimum, nighttime mean), according to model performance and association magnitudes between air pollutants and kidney-related conditions. In our main models, we adjusted for daytime mean outdoor wet-bulb globe temperature, which showed good model performance across all kidney-related conditions. We observed the odds ratios (ORs) for 5 μg/m3 increase in daily mean PM2.5 to be 1.013 (95% confidence interval [CI]: 1.001, 1.025) for AKF, 1.107 (95% CI: 1.018, 1.203) for GD, and 1.027 (95% CI: 1.015, 1.038) for volume depletion; and the OR for 5 ppb increase in daily 1-hour maximum NO2 to be 1.014 (95% CI; 1.008, 1.021) for AKF. We observed no associations with daily 8-hour maximum O3 exposure. Association estimates varied by adjustment for different intraday temperature measures: estimates adjusted for measures with poorer model performance resulted in the greatest deviation from estimates adjusted for daytime mean, especially for AKF and volume depletion. Our findings indicate that short-term exposure to PM2.5 and NO2 is a risk factor for specific kidney-related conditions and underscore the need for careful adjustment of temperature in air pollution epidemiologic studies.

A systematic review and meta-analysis of air pollution and angina pectoris attacks: identification of hazardous pollutant, short-term effect, and vulnerable population.

We conducted a systematic review and meta-analysis of global epidemiological studies of air pollution and angina pectoris, aiming to explore the deleterious air pollutant(s) and vulnerable sub-populations. PubMed and Web of Science databases were searched for eligible articles published between database inception and October 2021. Meta-analysis weighted by inverse-variance was utilized to pool effect estimates based on the type of air pollutant, including particulate matters (PM2.5 and PM10: particulate matter with an aerodynamic diameter ≤ 2.5µm and ≤ 10µm), gaseous pollutants (NO2: nitrogen dioxide; CO: carbon monoxide; SO2: sulfur dioxide, and O3: ozone). Study-specific effect estimates were standardized and calculated with percentage change of angina pectoris for each 10µg/m3 increase in air pollutant concentration. Twelve studies involving 663,276 angina events from Asia, America, Oceania, and Europe were finally included. Meta-analysis showed that each 10µg/m3 increase in PM2.5 and PM10 concentration was associated with an increase of 0.66% (95%CI: 0.58%, 0.73%; p < 0.001) and 0.57% (95%CI: 0.20%, 0.94%; p = 0.003) in the risk of angina pectoris on the second day of exposure. Adverse effects were also observed for NO2 (0.67%, 95%CI: 0.33%, 1.02%; p < v0.001) on the second day, CO (0.010%, 95%CI: 0.006%, 0.014%; p < 0.001). The elderly and patients with coronary artery disease (CAD) appeared to be at higher risk of angina pectoris. Our findings suggest that short-term exposure to PM2.5, PM10, NO2, and CO was associated with an increased risk of angina pectoris, which may have implications for cardiologists and patients to prevent negative cardiovascular outcomes.

Well Played: Using Game App Data to Assess Wildfire Smoke and Cognitive Performance

Well Played: Using Game App Data to Assess Wildfire Smoke and Cognitive Performance

Association between PM2.5 and respiratory hospitalization in Rio Branco, Brazil: Demonstrating the potential of low-cost air quality sensor for epidemiologic research.

Background.There is currently a scarcity of air pollution epidemiologic data from low- and middle-income countries (LMICs) due to the lack of air quality monitoring in these countries. Additionally, there is limited capacity to assess the health effects of wildfire smoke events in wildfire-prone regions like Brazil's Amazon Basin. Emerging low-cost air quality sensors may have the potential to address these gaps.Objectives.We investigated the potential of PurpleAir PM2.5 sensors for conducting air pollution epidemiologic research leveraging the United States Environmental Protection Agency's United States-wide correction formula for ambient PM2.5.Methods.We obtained raw (uncorrected) PM2.5 concentration and humidity data from a PurpleAir sensor in Rio Branco, Brazil, between 2018 and 2019. Humidity measurements from the PurpleAir sensor were used to correct the PM2.5 concentrations. We established the relationship between ambient PM2.5 (corrected and uncorrected) and daily all-cause respiratory hospitalization in Rio Branco, Brazil, using generalized additive models (GAM) and distributed lag non-linear models (DLNM). We used linear regression to assess the relationship between daily PM2.5 concentrations and wildfire reports in Rio Branco during the wildfire seasons of 2018 and 2019.Results.We observed increases in daily respiratory hospitalizations of 5.4% (95%CI: 0.8%, 10.1%) for a 2-day lag and 5.8% (1.5%, 10.2%) for 3-day lag, per 10 μg/m3 PM2.5 (corrected values). The effect estimates were attenuated when the uncorrected PM2.5 data was used. The number of reported wildfires explained 10% of daily PM2.5 concentrations during the wildfire season.Discussion.Exposure-response relationships estimated using corrected low-cost air quality sensor data were comparable with relationships estimated using a validated air quality modeling approach. This suggests that correcting low-cost PM2.5 sensor data may mitigate bias attenuation in air pollution epidemiologic studies. Low-cost sensor PM2.5 data could also predict the air quality impacts of wildfires in Brazil's Amazon Basin.

Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes.

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error. To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits. We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics. Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging). The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies. This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

Association between Outdoor Air Pollution Exposure and Handgrip Strength: Findings from the French CONSTANCES Study.

Association between Outdoor Air Pollution Exposure and Handgrip Strength: Findings from the French CONSTANCES Study.

Prediction of personal exposure to PM2.5 in mother-child pairs in rural Ghana.

Background:Air pollution epidemiological studies usually rely on estimates of long-term exposure to air pollutants, which are difficult to ascertain. This problem is accentuated in settings where sources of personal exposure differ from those of ambient concentrations, including household air pollution environments where cooking is an important source.Objective:The objective of this study was to assess the feasibility of estimating usual exposure to PM2.5 based on short term measurements.Methods:We leveraged three types of short-term measurements from a cohort of mother-child pairs in 26 communities in rural Ghana: (A) personal exposure to PM2.5 in mothers and age four children, ambient PM2.5 concentrations (B) at the community level and (C) at a central site. Baseline models were linear mixed models with a random intercept for community or for participant. Lowest root-mean-square-error (RMSE) was used to select the best performing model.Results:We analyzed 240 community-days and 251 participant-days of PM2.5. Medians (IQR) of PM2.5 were 19.5 (36.5) μg/m3 for the central site, 28.7 (41.5) μg/m3 for the communities, 70.6 (56.9) μg/m3 for mothers, and 80.9 (74.1) μg/m3 for children. The ICCs (95% CI) for community ambient and personal exposure were 0.30 (0.17, 0.47) and 0.74 (0.65, 0.81) respectively. The sources of variability differed during the Harmattan season. Children’s daily exposure was best predicted by models that used community ambient compared to mother’s exposure as a predictor (log-scale RMSE: 0.165 vs 0.325).Conclusion:Our results support the feasibility of predicting usual personal exposure to PM2.5 using short-term measurements in settings where household air pollution is an important source of exposure. Our results also suggest that mother’s exposure may not be the best proxy for child’s exposure at age four.

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study

BackgroundRecent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people’s homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.ObjectiveThis simulation study aimed to understand the impact of incomplete addresses on health effect estimation based on the association between particulate matter with diameter ≤10 µm (PM10) and low birth weight (LBW).MethodsWe generated true annual average concentrations of PM10 at 46,007 mothers’ homes and their LBW status, using the parameters obtained from our data analysis and a previous study in Seoul, Korea. Then, we hypothesized that mothers’ address information is limited to the district and compared the properties of their health effect estimates of PM10 with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM10 and nine exposure prediction methods that provide different sets of predicted PM10 concentrations of mothers.ResultsWe observed increased bias and root mean square error consistently across all environmental scenarios and prediction methods using incomplete addresses compared to complete addresses. However, the bias related to incomplete addresses decreased when we used population-representative exposures averaged to the district from predicted PM10 at census tract centroids.SignificanceOur simulation study suggested that individual exposure estimated by prediction approaches and averaged across population-representative points can provide improved accuracy in health effect estimates when complete address data are unavailable.Impact statementOur simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people’s addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, district, and zip code area. Our findings can help understand the possible consequences of limited address information and provide practical guidance in achieving the accuracy in health effect analysis.

Spatiotemporal estimation of the PM2.5 concentration and human health risks combining the three-dimensional landscape pattern index and machine learning methods to optimize land use regression modeling in Shaanxi, China

PM2.5 pollution endangers human health and urban sustainable development. Land use regression (LUR) is one of the most important methods to reveal the temporal and spatial heterogeneity of PM2.5, and the introduction of characteristic variables of geographical factors and the improvement of model construction methods are important research directions for its optimization. However, the complex non-linear correlation between PM2.5 and influencing indicators is always unrecognized by the traditional regression model. The two-dimensional landscape pattern index is difficult to reflect the real information of the surface, and the research accuracy cannot meet the requirements. As such, a novel integrated three-dimensional landscape pattern index (TDLPI) and machine learning extreme gradient boosting (XGBOOST) improved LUR model (LTX) are developed to estimate the spatiotemporal heterogeneity in the fine particle concentration in Shaanxi, China, and health risks of exposure and inhalation of PM2.5 were explored. The LTX model performed well with R2 = 0.88, RMSE of 8.73 μg/m3 and MAE of 5.85 μg/m3. Our findings suggest that integrated three-dimensional landscape pattern information and XGBOOST approaches can accurately estimate annual and seasonal variations of PM2.5 pollution The Guanzhong Plain and northern Shaanxi always feature high PM2.5 values, which exhibit similar distribution trends to those of the observed PM2.5 pollution. This study demonstrated the outstanding performance of the LTX model, which outperforms most models in past researches. On the whole, LTX approach is reliable and can improve the accuracy of pollutant concentration prediction. The health risks of human exposure to fine particles are relatively high in winter. Central part is a high health risk area, while northern area is low. Our study provides a new method for atmospheric pollutants assessing, which is important for LUR model optimization, high-precision PM2.5 pollution prediction and landscape pattern planning. These results can also contribute to human health exposure risks and future epidemiological studies of air pollution.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

A discussion of competing events in epidemiologic studies of air pollution

BACKGROUND AND AIM: Adverse health impacts of air pollution are well-documented in epidemiologic studies. However, one inferential challenge in estimating the effect of air pollution on chronic conditions arises from competing events. In observational studies, other health outcomes may preclude or “compete” with the outcome of interest. Although a variety of approaches and frameworks to consider competing events have been described in epidemiologic literature, they are underutilized in studies of air pollution. This research aims to present case studies demonstrating competing events and provides solutions to address inferential questions. METHODS: Three approaches were demonstrated to account for competing events, each with unique assumptions and target estimands. First, a controlled direct effect, not mediated by the competing event, was estimated with a cause-specific hazard ratio. Next, the total effect, including pathways through the competing event was estimated with a subdistribution hazard ratio. Finally, inverse probability weights were applied to correct for time-varying informed censoring due to a competing event and estimate a weighted cause-specific hazard ratio. Various sensitivity analyses will also be discussed. RESULTS:Findings from this study underscore the limited consideration of competing events in epidemiologic studies of air pollution and highlight three approaches to account for competing events. CONCLUSIONS:Consideration of competing events will allow for more robust inferences from epidemiologic studies. We provide recommendations for future investigators considering competing events in epidemiologic studies of air pollution and chronic conditions. KEYWORDS: Air pollution, epidemiology, causal inference

Estimation of long-term exposure to PM2.5 based on short-term personal measurements in mother-child pairs in rural Ghana

BACKGROUND AND AIM: Air pollution epidemiological studies usually rely on estimates of long-term exposure to air pollutants, which are difficult to ascertain. This problem is accentuated in low- and middle-income countries where sources of personal exposure often differ from those of ambient concentrations. METHODS: We used a well-characterized cohort of mother and child pairs in 29 communities in rural Ghana and evaluated modeled long-term predictions of PM2.5 exposure. The models relied on three types of short-term measurements: personal exposure to PM2.5 in mother-child pairs, ambient PM2.5 concentration at the community level, and ambient PM2.5 concentration at a central site. Intra-class correlation coefficients (ICC) were computed to assess sources of variability in community ambient and personal exposure. Empty models were linear mixed models with a random intercept for the community or for the participant. Additional predictors included time-varying factors for the community and time-invariant characteristics for the household and participant. Personal exposure in mother and children were modeled separately. Models were evaluated using leave-one-out cross validation and the lowest root-mean-square-error (RMSE) was used to select the best performing model. RESULTS:We analyzed 240 community-days and 251 participant-days of PM2.5. Means (sd) of daily PM2.5 were 30.2 (24.9) μg/m³ for the central site, 43.5 (36.2) μg/m³ for the communities, 83.0 (53.2) μg/m³ for mothers, and 82.3 (43.6) μg/m³ for children. The ICCs (95% CI) for community ambient and personal exposure were 0.30 (0.17, 0.47) and 0.74 (0.65, 0.81) respectively. The sources of variability differed during the Harmattan season. Children’s daily exposure was best predicted by models that used community ambient compared to mother’s exposure as a predictor (log-scale RMSE: 0.165 vs 0.325). CONCLUSIONS:Our results support the feasibility of predicting long-term personal exposure to PM2.5 using short-term measurements in settings similar to rural Ghana and suggest that mother’s exposure may not be the best proxy for child’s exposure. KEYWORDS: Particulate matter, long-term exposure, children's environmental health

P–736 Elevated levels of ambient air pollutants increase the primary sex ratio in human embryos

Abstract Study question Are there any associations between ambient outdoor air pollution and the primary sex ratio (PSR)? Summary answer Short-term exposure to increased PM10, PM2.5 and NO2 levels were significantly associated with higher PSR. What is known already PSR estimates represent a backward extrapolation from data based on spontaneous or induced abortions, fetal deaths or live births and are usually male-biased. A recent study, analyzing 3- to 6-day-old embryos derived from assisted reproductive technology (ART) procedures, showed that the sex ratio at conception is unbiased (0.5). Epidemiologic studies of air pollution on secondary (birth) sex ratio showed that higher levels of particulate pollution were associated with increased rates of female birth. However, a direct association between urban levels of air pollutants and PSR has not been reported. Study design, size, duration A retrospective cohort study was carried out to assess the impact of long- or short-term exposure to six ambient outdoor air pollutants (particulate matter, PM10µm and PM2.5µm; SO2; CO; NO2; O3) on PSR (XY/XX) of couples undergoing their first IVF cycle for preimplantation genetic screening (N = 337). Data was from fixed air quality monitoring stations across the city between January 2014 and December 2018. Embryos with sex chromosome abnormalities were excluded from the analysis. Participants/materials, setting, methods Average concentrations of the pollutants for the 90 (long-term exposure) and 15 days (short-term exposure) predating oocyte retrieval represented the exposures of interest. Pollutant levels were categorized into quartiles (Q1 to Q4) and exposure risk was divided into two periods in which average concentrations and confidence intervals for the pollutants were in the upper quartile (Q4 period) or not (Q1-Q3 period). The strength association between exposure risk and PSR was performed through analysis of covariance. Main results and the role of chance The estimated means of PM10, PM2.5, SO2, NO2, O3 and CO for Q1-Q3/Q4 periods were 27.7/39.3, 16.7/23.7, 2.5/3.9, 37.0/46.4, 32.2/45.3 µg/m3 and 0.64/0.87 ppm and 26.3/43.0, 16.0/26.3, 2.4/4.2, 36.5/47.8, 31.7/50.4 µg/m3 and 0.62/0.90 ppm for long- and short-term exposures, respectively. PM10, PM2.5 and NO2 levels in the Q4 period had significantly higher PSR (138.1, 134.0 and 137.6) when compared to Q1-Q3 period (94.4, 98.1 and 96.4) for the short-term exposure (p = 0.0193; p = 0.0439; p = 0.0180, respectively). PM10, PM2.5, SO2, NO2 and CO levels in the Q4 and Q1-Q3 periods for the long-term exposure showed no significant effect on PSR. Contrastingly, O3 levels in the Q4 period had significantly lower PSR (82.6) when compared to Q1-Q3 (115.9) for the long-term exposure (p = 0.0202). A monotonic increase in PSR was observed with increased PM10 concentration in the Q4 period for the short-term exposure (F-ratio: 4.4476; p = 0.0352). Limitations, reasons for caution Some limitations of the study should be underlined, such as its retrospective nature, exposure assessment based on pollutant levels derived from a network average across city sites, and limited extrapolation of the results to the general population. Wider implications of the findings: Our data suggest that short-term exposure to environmental factors could affect the primary sex ratio in polluted seasons or cities. A monotonic effect on PSR in the case of exposure to increasing PM10 levels was identified. Trial registration number Not applicable

P-736 Elevated levels of ambient air pollutants increase the primary sex ratio in human embryos

Abstract Study question Are there any associations between ambient outdoor air pollution and the primary sex ratio (PSR)? Summary answer Short-term exposure to increased PM10, PM2.5 and NO2 levels were significantly associated with higher PSR. What is known already PSR estimates represent a backward extrapolation from data based on spontaneous or induced abortions, fetal deaths or live births and are usually male-biased. A recent study, analyzing 3- to 6-day-old embryos derived from assisted reproductive technology (ART) procedures, showed that the sex ratio at conception is unbiased (0.5). Epidemiologic studies of air pollution on secondary (birth) sex ratio showed that higher levels of particulate pollution were associated with increased rates of female birth. However, a direct association between urban levels of air pollutants and PSR has not been reported. Study design, size, duration A retrospective cohort study was carried out to assess the impact of long- or short-term exposure to six ambient outdoor air pollutants (particulate matter, PM10µm and PM2.5µm; SO2; CO; NO2; O3) on PSR (XY/XX) of couples undergoing their first IVF cycle for preimplantation genetic screening (N = 337). Data was from fixed air quality monitoring stations across the city between January 2014 and December 2018. Embryos with sex chromosome abnormalities were excluded from the analysis. Participants/materials, setting, methods Average concentrations of the pollutants for the 90 (long-term exposure) and 15 days (short-term exposure) predating oocyte retrieval represented the exposures of interest. Pollutant levels were categorized into quartiles (Q1 to Q4) and exposure risk was divided into two periods in which average concentrations and confidence intervals for the pollutants were in the upper quartile (Q4 period) or not (Q1-Q3 period). The strength association between exposure risk and PSR was performed through analysis of covariance. Main results and the role of chance The estimated means of PM10, PM2.5, SO2, NO2, O3 and CO for Q1-Q3/Q4 periods were 27.7/39.3, 16.7/23.7, 2.5/3.9, 37.0/46.4, 32.2/45.3 µg/m3 and 0.64/0.87 ppm and 26.3/43.0, 16.0/26.3, 2.4/4.2, 36.5/47.8, 31.7/50.4 µg/m3 and 0.62/0.90 ppm for long- and short-term exposures, respectively. PM10, PM2.5 and NO2 levels in the Q4 period had significantly higher PSR (138.1, 134.0 and 137.6) when compared to Q1-Q3 period (94.4, 98.1 and 96.4) for the short-term exposure (p = 0.0193; p = 0.0439; p = 0.0180, respectively). PM10, PM2.5, SO2, NO2 and CO levels in the Q4 and Q1-Q3 periods for the long-term exposure showed no significant effect on PSR. Contrastingly, O3 levels in the Q4 period had significantly lower PSR (82.6) when compared to Q1-Q3 (115.9) for the long-term exposure (p = 0.0202). A monotonic increase in PSR was observed with increased PM10 concentration in the Q4 period for the short-term exposure (F-ratio: 4.4476; p = 0.0352). Limitations, reasons for caution Some limitations of the study should be underlined, such as its retrospective nature, exposure assessment based on pollutant levels derived from a network average across city sites, and limited extrapolation of the results to the general population. Wider implications of the findings Our data suggest that short-term exposure to environmental factors could affect the primary sex ratio in polluted seasons or cities. A monotonic effect on PSR in the case of exposure to increasing PM10 levels was identified. Trial registration number Not applicable

Estimations of ambient fine particle and ozone level at a suburban site of Beijing in winter

Estimates of PM2.5 and O3 in suburban areas are of importance for assessing exposure risk and epidemiological studies of air pollution where large scale and long-term measurements network are absent. To fulfill this goal, our study develops a flexible approach to predict levels of PM2.5 and O3 at a suburban site of Beijing using multilayer perceptron (MLP) neural network analysis with the inputs of gaseous air pollutants (CO, SO2, NO, and NO2) and meteorological parameters (wind direction, wind speed, temperature, pressure and humidity). Daily ambient data of PM2.5, O3, PM10, CO, SO2, NO, and NO2 were estimated using hourly data collected from January 20 to March 10 in the years from 2016–2020 at a suburban site of Beijing, respectively. Ambient measured levels of PM2.5 and O3 were compared with the output estimates of PM2.5 and O3 through MLP neural network analysis with limited input variables. Overall, MLP neural network analysis could explain 97% of measured PM2.5 mass and 82% of measured O3 level with R2 values of 0.983 and 0.905, respectively. This approach could be helpful for reconstruct historical PM2.5 and O3 levels in suburban areas.

Linking air quality, health, and economic effect models for use in air pollution epidemiology studies with uncertain factors

Epidemiological studies have shown that premature mortality, hospital admission, and outpatient visits are closely related to particulate matter (PM) pollution. The modelling of air quality, assessment of human health impact, and estimation of health impact models are useful tools for assessing the impact of air pollution on health. This study proposes a model framework for assessing air pollution damage, including air quality simulation for PM10, assessment of human health impacts, and estimation of health impact models that meet the requirements of actual monitoring data. The novel contribution of this study is a proposal for the application of the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality Modeling System (CMAQ) model and the epidemiological coefficient β of concentration–response functions (CRFs), which were determined from epidemiological studies and selected in accordance with the study area conditions under limited conditions related to the field measurement data, to calculate the health impacts and economic losses caused by PM10. Calculation results for a typical month in 2017 showed that economic losses were approximately 125.119 million Vietnamese Dong (95% CI: −26.841, 272.633), in which the highest number of mortality and morbidity due to PM10 dust pollution exposure was identified on October 24, 2017. The WRF/CMAQ simulation results were calibrated and validated to improve the efficiency of the estimation. These validated results were mainly based on the measured data, which played a significant role in enhancing the accuracy of the quantification of air pollution effects.

Confounding by Socioeconomic Status in Epidemiological Studies of Air Pollution and Health: Challenges and Opportunities.

Background:Despite a vast air pollution epidemiology literature to date and the recognition that lower-socioeconomic status (SES) populations are often disproportionately exposed to pollution, there is little research identifying optimal means of adjusting for confounding by SES in air pollution epidemiology, nor is there a strong understanding of biases that may result from improper adjustment.Objective:We aim to provide a conceptualization of SES and a review of approaches to its measurement in the U.S. context and discuss pathways by which SES may influence health and confound effects of air pollution. We explore bias related to measurement and operationalization and identify statistical approaches to reduce bias and confounding.Discussion:Drawing on the social epidemiology, health geography, and economic literatures, we describe how SES, a multifaceted construct operating through myriad pathways, may be conceptualized and operationalized in air pollution epidemiology studies. SES varies across individuals within the contexts of place, time, and culture. Although no single variable or index can fully capture SES, many studies rely on only a single measure. We recommend examining multiple facets of SES appropriate to the study design. Furthermore, investigators should carefully consider the multiple mechanisms by which SES might be operating to identify those SES indicators that may be most appropriate for a given context or study design and assess the impact of improper adjustment on air pollution effect estimates. Last, exploring model contraction and expansion methods may enrich adjustment, whereas statistical approaches, such as quantitative bias analysis, may be used to evaluate residual confounding. https://doi.org/10.1289/EHP7980

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China

Accurate estimation of daily spatially-continuous PM2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) μg/m3, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM2.5 relationships and elucidate the estimated PM2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM2.5 estimation but also other earth data and scenarios.