Air Quality System Research Articles

Prevalence of Sick Building Syndrome and Its Related Factors in Medical Staff in Iran: A Cross-Sectional Study

Background: Data on the prevalence of sick building syndrome (SBS) and its components in medical staff working at the Semnan University of Medical Sciences are limited. Objectives: The present study investigated the prevalence of SBS and its related factors in the medical staff in Semnan, Iran. Methods: A cross-sectional study was done on 300 medical staff in Kausar Hospital in Semnan, Iran, in 2020. A standard questionnaire was used to obtain information about SBS and indoor air quality. In order to measure environmental factors, random sampling was taken from different parts of the hospital, including building inspection, dust samples, and indoor and outdoor air measurements. The condition of physical factors, including temperature, humidity, and ventilation, was checked using relevant devices. The NIOSH 0800 method was used to measure the biological status of the working spaces in terms of fungal and bacterial contamination. Data analyses were performed using SPSS 26. Results: The mean age of the participants was 32.23 ± 5.34 years, of whom 81% were women. The prevalence of SBS was 65%, with the highest prevalence in the emergency ward (18%), followed by internal (16%), intensive care unit (13%), and cardiac care unit (12%). Sleep disorder (65%), tiredness (59%), feeling heavy (47%), headache (46%) and stress (45%) were the most common components of the SBS. There was a statistically significant relationship between the presence of harmful environmental factors, including noise, static electricity, unpleasant smell, and infection, and the prevalence of SBS (all P-values < 0.05). Conclusions: Considering the high prevalence of SBS in hospital employees, re-engineering the hospital environment and improving the air quality and ventilation systems may be useful in preventing SBS.

Design, Develop, Fabricate, and Statistical Quantification of Air Quality System Along with Web Application

Our Air Quality Index (AQI) measurement and analytics system is specifically designed for household, office, or single-room use, addressing concerns related to pollution's impact on respiratory health and visibility. The system incorporates dedicated sensors such as the PM Sensor (PMS5003), the ozone sensor (MQ- 131), the VOC Sensor (MP503), the CO2 Sensor (MH- Z19B), and the temperature and humidity sensor (DHT22). These sensors communicate with a web application server through the ESP8266-01 Wi-Fi module. The sensor values are displayed on a TFT (Thin Film Transistor) display, ensuring continuous monitoring with a time delay. The Arduino Pro Mini Board, powered by an FTDI (Future Technology Devices International Ltd.) chip, facilitates the sensor readings by operating on specific protocols. The ESP-01 module provides internet capability, enabling the system to host a custom web server. The sensor data is utilized to generate graphs on a dashboard, presenting the air quality information in a visually accessible manner. The displayed values are aligned with the standard guidelines set by the Central Pollution Control Board (CPCB). Moreover, the system supports historical data retrieval through integration with a PostgreSQL database, allowing users to analyze air quality trends over time. By providing real-time monitoring, comprehensive analytics, and historical data insights, our AQI measurement and analytics system empowers users to make informed decisions to improve air quality and mitigate the potential risks associated with pollution.

US air pollution is associated with increased incidence of non-syndromic cleft lip/palate

The incidence of NSCLP and NSCP per 1000 live births from 2016 to 2020 was extracted from the Centers for Disease Control and Prevention Vital Statistics Database and merged with national reports on air pollution using the Environmental Protection Agency Air Quality Systems annual data. The most commonly reported pollutants were analyzed including benzene, sulfur dioxide (SO2), particulate matter (PM) 2.5, PM 10, ozone (O3), and carbon monoxide (CO). Multivariable negative binomial and Poisson log-linear regression models evaluated the incidence of NSCLP and NSCP as a function of the pollutants, adjusting for race. All p-values are reported with Bonferroni correction. The median NSCLP incidence was 0.22/1000 births, and isolated NSCP incidence was 0.18/1000 births. For NSCLP, SO2 had a coefficient estimate (CE) of 0.60 (95% CI [0.23, 0.98], p<0.007) and PM 2.5 had a CE of 0.20 (95% CI [0.10, 0.31], p<0.005). Among isolated NSCP, no pollutants were found to be significantly associated. SO2 and PM 2.5 were significantly correlated with increased incidence of NSCLP. The American people and perinatal practitioners should be aware of the connection to allow for risk reduction and in utero screening.

Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States.

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

Spatiotemporal trends in PM2.5 chemical composition in the conterminous U.S. during 2006–2020

The spatiotemporal variations of fine particulate matter (PM2.5) chemical composition have changed over time in the U.S. and increasing evidence indicated differential toxicity of PM2.5 chemical composition. Thus, comprehensive explanation of PM2.5-related adverse health impacts in the U.S. necessitated a detailed analysis of spatiotemporal trends of PM2.5 chemical composition. This research aims to analyze the changes in concentrations of PM2.5 and its chemical composition in spatial and temporal scales in the conterminous U.S. The PM2.5 mass concentration and chemical speciation data were downloaded from U.S. EPA Air Quality System (AQS) (2006–2020) to investigate the spatiotemporal changes of PM2.5 and its chemical components. The results indicated that national annual average PM2.5 concentration was significantly reduced from 11.38 ± 2.94 μg m−3 in 2006 to 8.20 ± 2.76 μg m−3 in 2020 with an average reduction of 0.21 μg m−3yr−1, mainly attributed to inorganic PM2.5 reductions (i.e., ammonium (NH4+), nitrate (NO3−), and sulfate (SO42−)) and the average reductions were 0.09 μg m−3yr−1, 0.02 μg m−3yr−1, and 0.06 μg m−3yr−1, respectively. The largest air quality improvements occurred in areas with the worst baseline air quality. Moreover, observed spikes in PM2.5 in California in 2020 were due to higher concentrations of organic matter (OM) and elemental carbon (EC) caused by 2020 wildfires. Furthermore, while levels of SO42−, NO3−, and NH4+ almost levelled off in recent years, further air quality improvements may require targeting carbonaceous species. The heavily polluted days occurred less frequently in recent years and primary organic carbon (OC) accounted for a larger portion of OC in winter than in summer because of the relatively reduced formation rate of secondary organic aerosol (SOA). Our analysis revealed the spatial and temporal trends of various PM2.5 chemical composition in the conterminous U.S. and provided insights into source contributions, atmospheric chemical conditions, and development of future emissions control strategies.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

Wireless CO2 and Ozone Monitoring with IoT Technology for Indoor Air Quality

This article presents the development of a wireless sensing and control system for indoor air quality by measuring gases such as carbon dioxide, as well as the use of ozone as a biocide agent for environmental control in real time, respecting concentrations allowed by national and international environmental health standards. The developed system uses cutting edge technology such as the Internet of Things and Cloud Computing that allows remote access to air quality measurements through mobile devices with the installation of the developed App, which has the capacity for registration and controlled access to users, auto-configuration of devices and data visualization in real time and historical record. The use of ozone as a biocide agent for environmental control was effective against the use of chemicals or other substances since it does not leave hazardous residues, inhibiting the generation of new colonies and keeping the areas under a disinfection control.

Special Issue: Advances on Indoor Air Quality Systems for Healthy and Sustainable Buildings

Abstract This special issue is an outcome of a Workshop “Impact of Sustainable Buildings in Arid Environment on the Indoor and Outdoor Air Quality,” held in Doha, Qatar, on May 17–19, 2022 with the participation of more than 50 experts in the field . We reported the highlights of the workshop in the February 2023 issue of this Journal [2]. There was a great response to the call for papers and eight manuscripts were accepted and selected for this Special Issue. These articles are authored by an outstanding roster of experts in their respective fields and tackle various research gaps that impact the quality of our indoor environments. The topics of this Special Issue spanned a large area that included the use of microchannels as a viable building insulation technique [3], enhancing the energy storage capabilities for solar-powered air conditioning systems [4], the impact of outdoor environment on the indoor air quality of commercial buildings using natural ventilation [5], utilizing machine learning for optimum manufacturing processes scheduling [6], utilizing a decision support tool for HAVC filters selection [7], Decarbonization through electrification [8] improving demand-response flexibilities in pharmaceutical buildings using optimization software, [9] and methodology for predicting the energy consumption of buildings using deep neural networks [10].

Multi-year observations of particulate matter and gases over Mumbai: Spatio-temporal variation, oxidation ratios, and secondary aerosols

System of Air quality and weather Forecasting And Research (SAFAR-Mumbai) network hourly observations of air pollutants spanning half a decade are subjected to a Generalized Additive Model (GAM) to investigate the non-linear relationship of PM2.5 to other precursor gases and meteorological parameters. The Effective Degrees of Freedom (EDF), a statistic of GAM quantifying the non-linear relationship, reveals that PM2.5 is highly non-linearly dependent on NH3, SO2, and CO as inferred from the response curves exhibiting at least one inflexion point and threshold responses. PM2.5 is linearly related to NO2 (EDF = 1), while it is non-linearly related to other gases and meteorological parameters (EDF > 2). The frequency distribution of PM2.5 shows a sharp peak at 25 μg m−3 in monsoon and broad peaks in pre-monsoon (peak at 50 μg m−3), post-monsoon (peak at 60 μg m−3) and winter (peak at 100 μg m−3) indicating relatively large variability in winter and post-monsoon. Chemical analysis of PM2.5 shows a high contribution of four major ions in the order of SO42− > Cl− > Na+ > NH4+, mainly associated with anthropogenic and marine sources. The average sulphate oxidation ratio (SOR), nitrogen oxidation ratio (NOR), and neutralization ratio (NR) are up to 0.3, 0.04, and 0.27, respectively, indicating that SO2 is contributing majorly to secondary aerosol production. Monthly concertation of PM2.5, Ozone (O3), CO, NO2, SO2, and NH3 shows U-shaped variability with maximum in winter and minimum in monsoon.

Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020

Abstract. Tropospheric nitrogen dioxide (NO2) concentrations have declined dramatically over the United States (USA) and Europe in recent decades. Here we investigate the changes in surface and free-tropospheric O3 accompanied by NO2 changes over the USA and Europe in 2005–2020 by assimilating the Ozone Monitoring Instrument (OMI) and U.S. Air Quality System (AQS) and European AirBase network O3 observations. The assimilated O3 concentrations demonstrate good agreement with O3 observations. Surface O3 concentrations are 41.4, 39.5, and 39.5 ppb (parts per billion; USA) and 35.3, 32.0, and 31.6 ppb (Europe) and tropospheric O3 columns are 35.5, 37.0, and 36.8 DU (USA) and 32.8, 35.3, and 36.4 DU (Europe) in the simulations, assimilations, and observations, respectively. We find overestimated summertime surface O3 concentrations over the USA and Europe, which resulted in a surface O3 maximum in July–August in the simulations, which is in contrast to April in the observations. Furthermore, our analysis exhibits limited changes in surface O3 concentrations; i.e., they decreased by −6 % over the USA and increased by 1.5 % over Europe in 2005–2020. The surface observation-based assimilations suggest insignificant changes in tropospheric O3 columns, namely −3.0 % (USA) and 1.5 % (Europe) in 2005–2020. While the OMI-based assimilations exhibit larger decreases in tropospheric O3 columns, with −12.0 % (USA) and −15.0 % (Europe) in 2005–2020, the decreases mainly occurred in 2010–2014, corresponding to the reported slower decline in free-tropospheric NO2 since 2010. Our analysis thus suggests that there are limited impacts of the decline in local emissions on tropospheric O3 over the USA and Europe and advises more efforts to evaluate the possible contributions of natural sources and transport. The discrepancy in assimilated tropospheric O3 columns further indicates the possible uncertainties in the derived tropospheric O3 changes.

A Data-Fusion Approach to Assessing the Contribution of Wildland Fire Smoke to Fine Particulate Matter in California

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method to study wildfire contribution to PM2.5 using satellite-derived smoke plume indicators and PM2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy.

An integrated monitoring system (IMS) for air quality: Observations of a unique ozone-exceedance event in Maryland

Ozone (O3) exceedance events in the mid-Atlantic states of the U.S. are typically observed during the summer months. They frequently result from local diurnal emissions of NO2 and VOCs where photochemical production of O3 is most prevalent. Occasionally, summertime O3-exceedances in the region are driven by long-range transport of pollutants or regional nocturnal dynamics. Discussed herein is the use of multiple observation platforms as an integrated monitoring system (IMS) to characterize the spatiotemporal confluence of long-range transport and nocturnal low-level jet induced O3-exceedances impacting air quality in the mid-Atlantic region. The IMS discussed is comprised of enhanced monitoring of vertically resolved observations of O3, atmospheric backscatter, winds, and state variables (e.g., temperature and relative humidity). This is in combination with surface-O3 observations, satellite-retrieved aerosol optical depth (by the moderate resolution imaging spectroradiometer (MODIS)), and simulations of air mass transport by NOAA's hybrid single-particle Lagrangian trajectory (HYSPLIT) model. Here we discuss an exceedance event that impacted the Mid-Atlantic region on May 20, 2021, where seven air monitoring sites in Maryland were driven by the combination of nocturnal low-level jet downmixing of pollutants and long-range transport of biomass-burning pollutants/O3-precursors. The most notable features of this case are this event's deviation from decadal O3-exceedance seasonality trends, the large spatial context of high surface-O3 impacting traditionally non-exceedance sites (sites that have not exceeded in years), the arrival of a well-mixed polluted air mass into the region, and nocturnal increases in surface-O3 induced by NLLJ.

Roles of Artificial Intelligence and Machine Learning in Enhancing Construction Processes and Sustainable Communities

Machine Learning (ML), a subset of Artificial Intelligence (AI), is gaining popularity in the architectural, engineering, and construction (AEC) sector. This systematic study aims to investigate the roles of AI and ML in improving construction processes and developing more sustainable communities. This study intends to determine the various roles played by AI and ML in the development of sustainable communities and construction practices via an in-depth assessment of the current literature. Furthermore, it intends to predict future research trends and practical applications of AI and ML in the built environment. Following the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, this study highlights the roles that AI and ML technologies play in building sustainable communities, both indoors and out. In the interior environment, they contribute to energy management by optimizing energy usage, finding inefficiencies, and recommending modifications to minimize consumption. This contributes to reducing the environmental effect of energy generation. Similarly, AI and ML technologies aid in addressing environmental challenges. They can monitor air quality, noise levels, and waste management systems to quickly discover and minimize pollution sources. Likewise, AI and ML applications in construction processes enhance planning, scheduling, and facility management.

The short-term effect of ozone on pregnancy loss modified by temperature: Findings from a nationwide epidemiological study in the contiguous United States

BackgroundPregnancy loss, a major health issue that affects human sustainability, has been linked to short-term exposure to ground-surface ozone (O3). However, the association is inconsistent, possibly because of the co-occurrence of O3 and heat episodes, as increased temperature is a risk factor for pregnancy loss. To explain this inconsistency, the effect of O3 on pregnancy loss needs to be examined jointly with that of high temperature. MethodsA total of 247,305 pregnancy losses during the warm season were extracted from fetal death certificates from the 386 counties in contiguous United States from 1989 to 2005. We assessed environmental exposure based on the daily maximum 8 h average of O3 from Air Quality System monitors and the 24 h average temperature from the North American Regional Reanalysis product. We conducted a bidirectional, time-stratified case-crossover study of the association between pregnancy loss and exposures to O3 and temperature and their multiplicative interaction. The main time window for the exposure assessment was the day of case occurrence and the preceding 3 days. To estimate the association, we used conditional logistic regression with adjustment for relative humidity, height of the planetary boundary layer, and holidays. Sensitivity analyses were performed on the lagged structure, nonlinearity, and between-subpopulation heterogeneity of the estimated joint effect. ResultsThe joint effect was first estimated by the regression against categorical exposure by tertile. Compared to the low-low exposure group (O3 ≤ 78 μg/m3 and temperature ≤ 18 °C), the odds of pregnancy loss was significantly higher by 6.0 % (95 % confidence interval [CI] 2.4–9.7 %), 9.8 % (6.1–13.8 %), and 7.5 % (4.7–10.3 %) in the high-low (>104 μg/m3 and ≤18 °C), low-high (≤78 μg/m3 and >23 °C), and high-high (>104 μg/m3 and >23 °C) groups. The model of linear exposure and the multiplicative interaction yielded similar results. Each increment of 10 μg/m3 in O3 and 1 °C in temperature was associated with a 3.0 % (2.0 %–4.0 %) and 3.9 % (3.5 %–4.3 %), respectively, increase in the odds of pregnancy loss. A decrease in odds of 0.2 % (0.1 %–0.2 %) was associated with the temperature × O3 interaction. The finding of an antagonistic interaction between temperature and O3 was confirmed by models parametrizing the joint exposure as alternative nonlinear terms (i.e., a two-dimensional spline term or a varying-coefficient term) and was robust to a variety of exposure lags and stratifications. Therefore, the marginal effect of O3 was estimated to vary by climate zone. A significant association between O3 and pregnancy loss was observed in the northern, but not southern, United States. ConclusionJoint exposure to O3 and high temperature can increase the risk for pregnancy loss. The adverse effect of O3 is potentially modified by ambient temperature. In high-latitude cities, controlling for O3 pollution could protect maternal health.

Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm

It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate.

Traffic-related air pollution is associated with spontaneous extremely preterm birth and other adverse perinatal outcomes

Although there is growing awareness of the relationship between air pollution and preterm birth, limited data exist regarding the relationship with spontaneous preterm birth and severe neonatal outcomes. This study aimed to examine the association between traffic-associated air pollution exposure in pregnancy and adverse perinatal outcomes including extremes of preterm birth, neonatal intensive care unit admissions, low birthweight, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation. This was a retrospective cohort study of singleton pregnancies of patients residing in a metropolitan area in the southern United States. Using monitors strategically located across the region, average nitrogen dioxide concentrations were obtained from the Environmental Protection Agency Air Quality System database. For patients living within 10 miles of a monitoring station, average exposure to nitrogen dioxide was estimated for individual patients' pregnancy by trimester. Logistic regression models were used to assess the effect of pollutant exposure on gestational age at birth, indicated vs spontaneous delivery, and neonatal outcomes while adjusting for maternal age, self-reported race, parity, season of conception, diabetes mellitus, body mass index, registered Health Equity Index, and nitrogen dioxide monitor region. Adjusted odds ratios and 95% confidence intervals were calculated for an interquartile increase in average nitrogen dioxide exposure. Between January 1, 2013 and December 31, 2021, 93,164 patients delivered a singleton infant. Of these, 62,189 had measured nitrogen dioxide exposure during the pregnancy from a nearby monitoring station. Higher average nitrogen dioxide exposure throughout pregnancy was significantly associated with preterm birth (adjusted odds ratio, 1.94; 95% confidence interval, 1.77-2.12) and an increase in neonatal intensive care unit admissions, low birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation. This relationship persisted for nulliparous patients and spontaneous preterm birth, and had a greater association with earlier preterm birth. In a metropolitan area, increased exposure to the air pollutant nitrogen dioxide in pregnancy was associated with spontaneous preterm birth and had a greater association with extremely preterm birth. A greater association with neonatal intensive care unit admissions, low-birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation was found even in term infants.

Decadal growth in emission load of major air pollutants in Delhi

Abstract. The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM2.5, PM10, CO, NOx, VOC, SO2, BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Gg yr−1, respectively, for the year 2020. The decadal growth (2010–2020) in PM2.5 and PM10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at https://doi.org/10.5281/zenodo.7715595 (Sahu et al., 2023).

Can traffic-related air pollution trigger myocardial infarction within a few hours of exposure? Identifying hourly hazard periods

IntroductionTraffic-related air pollution can trigger myocardial infarction (MI). However, the hourly hazard period of exposure to nitrogen dioxide (NO2), a common traffic tracer, for incident MI has not been fully evaluated. Thus, the current hourly US national air quality standard (100 ppb) is based on limited hourly-level effect estimates, which may not adequately protect cardiovascular health. ObjectivesWe characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015. MethodsFor nine cities in NYS, we obtained data on MI hospitalizations from the NYS Department of Health Statewide Planning and Research Cooperative System and hourly NO2 concentrations from the US Environmental Protection Agency’s Air Quality System database. We used city-wide exposures and a case-crossover study design with distributed lag non-linear terms to assess the relationship between hourly NO2 concentrations over 24 h and MI, adjusting for hourly temperature and relative humidity. ResultsThe mean NO2 concentration was 23.2 ppb (standard deviation: 12.6 ppb). In the six hours preceding MI, we found linearly increased risk with increasing NO2 concentrations. At lag hour 0, a 10 ppb increase in NO2 was associated with 0.2 % increased risk of MI (Rate Ratio [RR]: 1.002; 95 % Confidence Interval [CI]: 1.000, 1.004). We estimated a cumulative RR of 1.015 (95 % CI: 1.008, 1.021) for all 24 lag hours per 10 ppb increase in NO2. Lag hours 2–3 had consistently elevated risk ratios in sensitivity analyses. ConclusionsWe found robust associations between hourly NO2 exposure and MI risk at concentrations far lower than current hourly NO2 national standards. Risk of MI was most elevated in the six hours after exposure, consistent with prior studies and experimental work evaluating physiologic responses after acute traffic exposure. Our findings suggest that current hourly standards may be insufficient to protect cardiovascular health.

A high-resolution inventory of ammonia emissions from agricultural fertilizer application and crop residue in Taiwan

The presence of ammonia (NH3) in the atmosphere has an impact on atmospheric nitrogen (N) deposition, air quality, and environmental systems. NH3 emissions in Taiwan are mainly driven by N fertilizer application and livestock operations, and their estimates are associated with considerable inaccuracies and uncertainties. In this study, a high-resolution (1 × 1 km) NH3 emissions inventory for agricultural fertilizers and crop residues in Taiwan was developed for 2016. The NH3 emissions were estimated based on detailed local activity data, such as seasonally varying planted areas and fertilizer application rates, and emissions from 88 different crops were resolved to better capture the seasonality and spatial heterogeneity. Emissions factors (EFs) are governed by ambient temperature, soil acidity and other factors. We further estimated NH3 emissions from two agricultural management scenarios, conventional farming (CF) and appropriate fertilization for crops (AF), to quantify the potential reduction in NH3 emissions due to government policy. The NH3 emissions from agricultural fertilizers and crop residues in Taiwan were approximately 25010 (20298.3) tons in 2016 for the CF (AF) scenario. Synthetic N fertilizer contributed 66.9% to the total emissions, followed by organic N fertilizer (32.1%), with crop residue (1.0%) being a relatively small source in the CF scenario. The NH3 emissions hotspots were located in the central to southern part of western Taiwan, where intense agricultural activities are conducted. The peak NH3 emissions occurred in spring and summer and were mainly associated with the cultivation of the first and second seasons of rice. We validated our estimated NH3 emissions with a satellite NH3 column, finding that the spatial-temporal variations in NH3 emissions from fertilizer application were in good coherency with the NH3 column concentration.

Additive dynamic models for correcting numerical model outputs

Numerical air quality models are pivotal for the prediction and assessment of air pollution, but numerical model outputs may be systematically biased. An additive dynamic model is proposed to correct large-scale raw model outputs using data from other sources, including readings collected at ground monitoring networks and weather outputs from other numerical models. An additive partially linear model specification is employed for the nonlinear relationships between air pollutants and covariates. In addition, a multi-resolution basis function approximate is proposed to capture the different small-scale variations of biases, and a discretized stochastic integro-differential equation is constructed to characterize the dynamic evolution of the random coefficients at each spatial resolution. An expectation-maximization algorithm is developed for parameter estimation and a multi-resolution ensemble-based scheme is embedded to accelerate the computation. For statistical inference, a conditional simulation technique is applied to quantify the uncertainty of parameter estimates and bias correction results. The proposed approach is used to correct the biased raw outputs of PM2.5 from the Community Multiscale Air Quality (CMAQ) system for China's Beijing-Tianjin-Hebei region. Our method improves the root mean squared error and continuous rank probability score by 43.70% and 34.76%, respectively. Compared to other statistical methods under different metrics, our model has advantages in both correction accuracy and computational efficiency.