Air Quality Monitoring Sites Research Articles

Acute cardiorespiratory response to air quality index in healthy young adults

BackgroundLittle is known about the acute health impacts of air quality index (AQI) on cardiorespiratory risk factors. ObjectivesTo assess the short-term links of AQI with cardiorespiratory risk factors in young healthy adults. MethodsWe performed a longitudinal panel study with 4 repeated visits in 40 healthy young adults in Hefei, Anhui Province, China from August to October 2021. Cardiorespiratory factors included systolic blood pressure (BP), diastolic BP (DBP), mean arterial pressure (MAP) and fractional exhaled nitric oxide (FeNO). We collected hourly AQI data from a nearby air quality monitoring site. Linear mixed-effects model was applied to assess the effects of AQI on BP and FeNO. ResultsThe study participants (75.0% females) provided 160 pairs of valid health measurements with average age of 24 years. The mean AQI level was 44.43 during the study period. There were significant positive associations of AQI with three BP parameters and FeNO at different lag periods. For example, an interquartile range increase in AQI (26.54 unit) over lag 0–24 h was associated with increments of 6.69 mmHg (95%CI: 2.95–10.44), 5.71 mmHg (95%CI: 3.30–8.13), 6.04 mmHg (95%CI: 3.46–8.62) and 5.67% (95%CI: 1.05%–16.05%) in SBP, DBP, MAP and FeNO, respectively. The results were robust after controlling for PM1. We did not find effect modifications by gender, BMI, physical activity, or AQI category level on the associations. ConclusionsThe current findings on associations of AQI with cardiorespiratory factors might add evidence of the acute adverse cardiorespiratory consequences following air pollution.

Relationships between Springtime PM2.5, PM10, and O3 Pollution and the Boundary Layer Structure in Beijing, China

Complex pollution with high aerosol and ozone concentrations has recently been occurring in several densely populated cities in China, raising concerns about the influence of meteorological factors, including synoptic circulation and local conditions. In this study, comprehensive analyses on the associations between PM2.5, PM10, and O3 and meteorological conditions were conducted based on observations from radar wind profiler, microwave radiometer, automatic weather station, and air quality monitoring sites in Beijing during the spring of 2019. The results showed that the boundary layer height and temperature inversion were negatively (positively) correlated with PM (O3) concentrations, modulating the degree of air pollution. Five identified synoptic patterns were derived using geopotential height data of the ERA5 reanalysis, among which Type 1, characterised by south-westerly prevailing winds with high pressure to the south, was considered to be associated with severe PM and O3 contamination. This indicates that air pollutants originating from southern regions exert a major influence on Beijing through the transportation effect. In addition, high temperature, relative humidity, and low wind velocity exacerbate pollution. Overall, this study provides significant information for understanding the vital roles played by meteorological elements at both the regional and local scales in regulating air contamination during spring in Beijing.

EVALUATION OF ALPHASENSE OPC-N2 SENSOR FOR PM10 MEASUREMENT IN THE NORTH JAKARTA

Spatial and temporal data of particulate matter (PM) are limited in Indonesia; hence cost-effective and robust instruments to monitor PM concentration could complete data coverage. The low-cost sensor (LCS) Alphasense OPC-N2 provides real-time PM concentration data and is relatively simple to install and deploy. This paper presents data from an OPC-N2 sensor collocated with a PM10 Beta Attenuation Monitor (BAM). The study was carried out at an air quality monitoring site in North Jakarta belonging to the provincial government. The location is considered suitable for evaluating the performance of OPC-N2 micro sensor as it is a representative of a typical Indonesian urbanized area with a range of pollutant sources, including sea-sourced aerosols. At the same site, a filter-based Gent Sampler (GS) measuring both PM10 and PM2.5 was also deployed. The study showed that 30-minutely and daily average concentrations data for PM10 measured by OPC-N2 were lower than that of BAM measurements in both averaged durations by approximately 50%. The comparison between OPC-N2 and GS for PM10 showed that OPC-N2 measurement was underestimated but it was overestimated for PM2.5. Nonetheless, correlations of OPC-N2 and BAM were 0.530 and 0.607 for PM10 and PM2.5, respectively. These results were comparable to other low-cost sensor evaluation studies in different countries, suggesting that the sensor can represent temporal variation of the reference measurement.

An extensive dust storm impact on air quality on 22 November 2018 in Sydney, Australia, using satellite remote sensing and ground data

Recurrent dust storms represent a significant concern in Australia because of their related hazards and damages since particulate matter (PM) has harmful impacts on the environmental, health and economic sectors. The particulate matter may be released from natural sources and human activities. The major part of natural particulate matter is emitted into the air by wind erosion processes from desert and semi-desert areas at the world scale. A huge dust storm crossed over several areas of New South Wales (NSW), Australia, including the Sydney region on 21–22 November 2018 and decreased the horizontal visibility to less than 1 km for 22 h. This study examined the synoptic weather conditions, and assessed the air quality and identified the source and transport trajectory of the dust storm over Sydney using ground and satellite remote sensing data. PM10 (< 10 μm) concentrations were obtained from selected air quality monitoring sites operated by the Environmental Protection Agency in NSW. The highest hourly concentration of PM10 (578.7 μg/m3) was recorded at Singleton in the Hunter Valley, while concentrations in Sydney ranged from 480 to 385 μg/m3, well above the standard air quality level in Australia (50 μg/m3 per 24 h). The HYSPLIT back trajectories of air parcels suggest that the potential sources of the dust episode originated from the Lake Eyre Basin and northeast South Australia, the Mundi Mundi plains west of Broken Hill, Cobar and the grazing lands and the red sandplains in northwestern NSW. It then travelled towards the east coast. These long-range airflows transported suspended dust particles, raising air quality to hazardous levels (elevated PM10 levels) over most areas of NSW. The results from the HYSPLIT model for dust movement are confirmed by MODIS satellite images. Many areas of NSW experienced this intense dust storm due to northwest wind generated by the low-pressure systems and cold fronts over South Australia and many parts of western NSW as it moved eastward.

Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas.

The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O3, NO2, PM2.5, and PM10 were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O3 (R2 = 0.36–0.97) and worst for NO2 (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O3 and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.

In-situ Measurement of Aerosol Water Content in an Urban Area Using a Sequential Aerosol-Water Measurement System (SAWMS)

Aerosol water content (AWC) significantly affects secondary aerosol formation and atmospheric visibility. Most ambient AWC values are obtained from models because the direct measurement is challenging. In this study, the sequential aerosol-water measurement system (SAWMS) was applied to measure AWC at the Xiaogang (XG) air-quality monitoring site of the Taiwan Environmental Protection Administration (TEPA), located in an industrialized seaport city. The relative humidity (RH) was set at 90% in the SAWMS during measurement, and the PM2.5 AWC was 39.0 ± 14.3 μg m-3 on average, which was 140% higher than the monitored PM2.5 average concentration. Water-soluble inorganic ions (WSIIs) were analyzed offline and used in ISORROPIA II to model the AWC. The modeled and measured AWC was well-correlated (R2 = 0.85; n = 39, p < 0.05), indicating that WSIIs contributed the most to AWC. During high AWC periods, NO3- concentrations were relatively higher, suggesting that NO3- was the predominant species contributing to AWC. Humidographs were constructed to analyze the AWC values under varying RH levels during the humidification and dehumidification processes for the three selected samples. The humidification results revealed a significant difference between the measured and modeled AWC within 60–80% RH. This might be due to deviations of aerosol combination types and the mixing state from atmospheric conditions. The modeled AWC was close to the measured AWC when the RH was over 80%. The nitrogen oxidation ratio and the AWC were well-correlated (R2 = 0.60) throughout the sampling period, implying that the measured AWC was beneficial to NO3- formation in the urban area. In summary, significant differences between modeled and measured AWC appeared during the humidification and dehumidification processes when the RH was below 80%, indicating that direct measurement of AWC under varying RH levels is still necessary.

Robust Spatiotemporal Estimation of PM Concentrations Using Boosting-Based Ensemble Models

Particulate matter (PM) as an air pollutant is harmful to the human body as well as to the ecosystem. It is crucial to understand the spatiotemporal PM distribution in order to effectively implement reduction methods. However, ground-based air quality monitoring sites are limited in providing reliable concentration values owing to their patchy distribution. Here, we aimed to predict daily PM10 concentrations using boosting algorithms such as gradient boosting machine (GBM), extreme gradient boost (XGB), and light gradient boosting machine (LightGBM). The three models performed well in estimating the spatial contrasts and temporal variability in daily PM10 concentrations. In particular, the LightGBM model outperformed the GBM and XGM models, with an adjusted R2 of 0.84, a root mean squared error of 12.108 μg/m2, a mean absolute error of 8.543 μg/m2, and a mean absolute percentage error of 16%. Despite having high performance, the LightGBM model showed low spatial prediction accuracy near the southwest part of the study area. Additionally, temporal differences were found between the observed and predicted values at high concentrations. These outcomes indicate that such methods can provide intuitive and reliable PM10 concentration values for the management, prevention, and mitigation of air pollution. In the future, performance accuracy could be improved through consideration of different variables related to spatial and seasonal characteristics.

Real-time estimation of PM2.5 concentrations at high spatial resolution in Busan by fusing observational data with chemical transport model outputs

PM2.5 pollution has attracted a lot of attention recently as the adverse effects on health it can cause are being recognised. Ambient PM2.5 data are usually obtained from two sources, the first source is observations collected from air quality monitoring networks, unfortunately, this data tends to be quite sparse due to the lack of coverage by these monitoring networks. The second source is the outputs from numerical modelling, having high spatial resolution at the level of grid cell, unfortunately with this approach little information on the uncertainty of those predicted values is available. In this study, we tackled these problems by developing a data fusion approach to predict PM2.5 concentrations on an hourly basis with high spatial resolution, this was done by regressing the observed data with the 1 km2 CMAQ-derived spatial coefficients after obtaining site-pairs from a seasonal cluster analysis. When applied to two extensive observational datasets (25 AQM sites and 21 mobile AQM sites) over a period from January 2019 to June 2020 these fusion-driven hourly PM2.5 predictions substantially outperformed (R = 0.80, RMSE = 8.23) the raw numerical model simulations (R = 0.55, RMSE = 12.91). Adopting an experimental bias-correction process further improved the prediction performance (R = 0.84, RMSE = 7.59). Applying this fusion approach to real-time PM2.5 predictions for the same 25 individual air quality monitoring sites over five months (July to November 2020) showed the excellent predictive power (R = 0.82, RMSE = 8.74) of our approach.

Impact of work resumption on air quality after subsiding of COVID-19: evidence from China

AbstractThis paper explores the short-run impact of work resumption, extensively launched on February 10, 2020 in China, on air quality after the subsiding of COVID-19. Utilizing the data of 1012 air-quality monitoring sites in 233 cities derived from the Real-time Release Air Quality Platform and the difference-in-differences method, we find that alternative measures of air quality index in non-Hubei provinces increase significantly, compared with those in Hubei province which was temporarily not allowed work resumption due to the severity of epidemic. Specifically, our results reveal a rise in AQI of 11.28 per cent, in PM2.5 of 12.47 per cent, in PM10 of 10.49 per cent, and in NO2 of 23.64 per cent, relative to the baseline mean. Moreover, the deterioration of air quality is found to be caused by intracity rather than intercity migration.

Temporal variations, regional contribution, and cluster analyses of ozone and NOx in a middle eastern megacity during summertime over 2017-2019.

Particulate matter is usually regarded as the dominant pollutant in Tehran megacity in Iran. However, the number of ozone exceedance days significantly increased in recent years. This study analyzes simultaneous measurements of O3 and NOx (NO+NO2) concentrations to improve our understanding of ozone evolution during the summers of 2017 to 2019. The k-means clustering technique was used to select five representative air quality monitoring sites in Tehran to capture O3 and NOx concentrations' variability. The findings show that all of the investigated sites failed to meet the ozone non-attainment criterion. The ozone weekend effect is seen in the study of weekday/weekend differences in 2017 and 2018, but not in 2019, which can be due to the shift in the ozone production regime. The summer mean variation analysis can also be used to deduce this regime change. In 2017, the O3 and NO2 summer mean variations suggest a holdback in the NO2 upward trend and a reversal in the O3 downward trend that had been in place since 2012. Air mass back trajectory clustering reveals that east and north-east air mass clusters have the most significant impact on Tehran's O3 pollution and the highest regional contribution to OX. The study of OX against NOx shows that the regional contribution to OX increased from 2017 to 2018 and then decreased in 2019; however, the local contribution is the opposite. The diurnal analysis of the regional and local contributions to OX indicated that OX in Tehran might be primarily affected by pollutants from a short distance. The findings reveal critical changes in the behavior of O3 in recent years, indicating that decision-makers in Tehran should reconsider air pollution control measures.

Spatiotemporal Distribution and Health Impacts of PM2.5 and O3 in Beijing, from 2014 to 2020

In China, fine particulate matter (PM2.5) and tropospheric ozone (O3) have become major air pollutants that threaten human health. Since 2013, the government has strengthened air pollution controls in Beijing and achieved significant effects. A spatial-temporal analysis was conducted of the distribution and health impacts of PM2.5 and O3 in Beijing, using data collected from 34 air quality monitoring sites between 2014 and 2020. In 2014, the annual average PM2.5 and seasonal (April to September) average of daily one-hourly maximum O3 concentrations (O3_max) were 92.0 μg·m-3 and 81.9 nmol·mol-1, respectively. From 2014 to 2020, annual average PM2.5 decreased at a rate of 7.5 μg·m-3. However, there was no significant difference in O3_max over the years. The concentrations of PM2.5 were highest in December and January (in winter) and lowest in August (in summer). On the contrary, O3_max was highest in June. The diurnal variations of PM2.5 were affected by meteorological conditions and emission sources, and maximum concentrations occurred between 22:00 to 00:00, while minimum concentrations occurred between 14:00 to 16:00. The concentration of O3_max showed an opposite pattern, with minimum vales occurring at 07:00 and maximum values occurring in the afternoon. The spatial distribution of PM2.5 showed similar patterns in 2014 and 2019, with the south of Beijing exhibiting the highest concentrations, and the north the lowest. The concentration of O3_max was higher in suburban areas than in traffic areas. In terms of health impacts, 1580 cases of cardiovascular disease and 821 of respiratory disease were attributed to PM2.5 in 2014, while 2180 cases of respiratory disease were attributable to O3 in 2014. In 2019, mortalities attributable to PM2.5 had decreased by 50% compared to 2014. While the number of disease cases attributable to O3 were similar in 2014 and 2019. the results indicate that PM2.5 pollution in Beijing has been successfully controlled, while O3 pollution has become more severe, and was the primary air pollutant threatening human health in 2019. Therefore, the synchronous control of PM2.5 and O3 should be implemented in the future.

A New Predictive Framework for Amazon Forest Fire Smoke Dispersion over South America

AbstractAerosol particles from forest fire events in the Amazon can be effectively transported to urban areas in southeastern South America, thus affecting the air quality over those regions. A combination of observational data and a comprehensive air quality modeling system capable of anticipating acute air pollution episodes is therefore required. A new predictive framework for Amazon forest fire smoke dispersion over South America has been developed based on the Weather Research and Forecasting with Chemistry community (WRF-Chem) model. Two experiments of 48-h simulations over South America were performed by using this system at 20-km horizontal resolution, on a daily basis, during August and September of 2018 and 2019. The experiment in 2019 included the very strong 3-week forest fire event, when the São Paulo metropolitan area, located in southeastern South America, was plunged into darkness on August 19. The model results were satisfactorily compared against satellite-based data products and in situ measurements collected from air quality monitoring sites. The system is executed daily immediately after the CPTEC Satellite Division releases the latest active fire locations data and provides 48-h forecasts of regional distributions of chemical species such as CO, PM2.5, and O3. The new modeling system will be used as a benchmark within the framework of the Chemistry of the Atmosphere–Field Experiment in Brazil (CAFE-Brazil) project, which will take place in 2022 over the Amazon.

Ground-level NO2 concentration estimation based on OMI tropospheric NO2 and its spatiotemporal characteristics in typical regions of China

Although the ground-level NO2 measurement from air quality monitoring sites is relatively accurate, it is a challenge to obtain continuous spatial coverage due to the discrete distribution of sites. Thus, the tropospheric column NO2 amount from satellites with wide spatial and temporal coverage and higher resolution has been increasingly used to estimate ground-level NO2. However, most estimation methods were performed spontaneously using a simple linear model throughout the study period. These simplified models improve the efficiency of model development and enhance the generality of the model application, but they ignore the fact that contributors to changes of ground-level NO2 are not always consistent with time. This study considered the fixed and random effects of influencing factors and developed a mixed effect model (MEM) to estimate the ground-level NO2. By using the data of tropospheric NO2 in China from January 1, 2014 to June 30, 2020 and other multivariate auxiliary data such as meteorological elements and terrain elevation, the reliability of daily ground-level NO2 in typical populated areas of China estimated by the MEM was evaluated. The average of monthly R2 of 10-fold CV in each study area during 2014–2020 is greater than 0.60 and the proportion of R2 greater than 0.7 is about 71%, suggesting the reliability of MEM. It is found that the ground-level NO2 distribution characteristics of each study area are more distinct, and the influential factors are also different. In addition, associated with the air quality control policies and emission reduction measures in various regions, the ground-level NO2 in each study area has shown an overall downward trend during 2014–2019. The uncertainty of daily-scale meteorological elements and boundary layer conditions can lead to varying degrees of deviations in daily-scale predictions of ground-level NO2. Validation with the station NO2 observations demonstrates that the ground-level NO2 prediction at seasonal time scale (R2 = 0.81, RMSE = 3.86 μg/m3) performs better than those at time scales of daily and monthly (R2 = 0.65, and 0.75, RMSE = 7.92, and 6.24 μg/m3). Therefore, the method of averaging can be used to improve the accuracy of ground-level NO2 predictions on individual dates. In summary, this study shows that MEM is a promising ground-level NO2 modeling method, and is effective for air pollution mapping in a large geographic region.

Evaluation of Using Satellite-Derived Aerosol Optical Depth in Land Use Regression Models for Fine Particulate Matter and Its Elemental Composition

This study introduced satellite-derived aerosol optical depth (AOD) in land use regression (LUR) modeling to predict ambient concentrations of fine particulate matter (PM2.5) and its elemental composition. Twenty-four daily samples were collected from 17 air quality monitoring sites (N = 408) in Taiwan in 2014. A total of 12 annual LUR models were developed for PM2.5 and 11 elements, including aluminum, calcium, chromium, iron, potassium, manganese, sulfur, silicon, titanium, vanadium, and zinc. After applied AOD and a derived-predictor, AOD percentage, in modeling, the number of models with leave-one-out cross-validation R2 &gt; 0.40 significantly increased from 5 to 9, indicating the substantial benefits for the construction of spatial prediction models. Sensitivity analyses of using data stratified by PM2.5 concentrations revealed that the model performances were further improved in the high pollution season.

Urban core-downwind differences and relationships related to ozone production in a major urban area in Texas

San Antonio, the second-most populous city in Texas and the seventh-most populous city in the United States (US), has been designated a marginal non-attainment area by the US Environmental Protection Agency with respect to the 2015 ozone (O3) National Ambient Air Quality Standard. While stationary air quality monitoring sites are operated in the region by the Texas Commission on Environmental Quality (TCEQ), there are limited in situ field measurements for O3 and its precursors in the urban core. To better understand O3 dynamics in San Antonio, a suite of meteorological and gas instruments was deployed during May 2017. We incorporate field measurements from two campaign sites and one TCEQ stationary monitoring site into a zero-dimensional O3 model to characterize the local formation and destruction rates of O3, hydroxyl radical (OH) reactivity of volatile organic compounds (VOCs), O3 production efficiency, and O3 formation regime in the urban core and directly downwind of San Antonio. Upwind/downwind differences indicate the importance of photochemical processing of VOCs with carbon-carbon double bonds. San Antonio was mostly in a nitrogen oxide (NOX)-sensitive regime throughout the daytime during the campaign period, with O3 formation peaking at noon in the city center and early afternoon at the downwind region. Formaldehyde (HCHO), isoprene, and alkenes dominated VOC reactivity, with alkenes and isoprene from San Antonio's core (upwind) likely contributing to the downwind formation of HCHO and enhancing its OH reactivity. However, their direct impact on downwind O3 production was not observed. Model results suggest further strengthening NOX emission controls to decrease O3 formation in San Antonio.

Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

The World Health Organization has been making recommendations on assessing and monitoring air quality for human health protection. Its implementation implies a spatial distribution plan for air quality monitoring sites, especially in populated cities where high emissions of air pollutants with anthropogenic origin are observed. Therefore, the present study aimed: (i) to propose the spatial distribution of air quality monitoring sites in the city of Luanda (capital of Angola); and (ii) to determine the PM2.5, PM10, NO2, SO2 and CO concentrations at two monitoring sites (Avenida Deolinda Rodrigues, ADR, and Condomínio Vale do Talatona, CVT), during four weeks for each site. Due to the absence of national legislation, air quality measurements were then compared to the limits defined by the European Union through Directive, 2008/50/EC. At ADR site, the average concentrations for PM10, PM2.5 and SO2 exceeded the limits recommended for human health. At CVT site, CO, NO2, PM10, PM2.5 and SO2 concentrations were lower than the values measured at ADR site, due to the relative location of important fixed air pollutant emission sources: airport, thermoelectric plants, refinery, cement plants and the Port of Luanda. The weekend effect was also assessed at both sites. At ADR site, NO2, SO2, PM2.5 and PM10 concentrations were higher at weekends than on weekdays. At CVT site, NO2, SO2 and PM2.5 concentrations were higher on weekdays compared to weekends. The present research will contribute to the decision-making process by the environmental health regulator in Luanda.

Bacteria and Their Antibiotic Resistance Profiles in Ambient Air in Accra, Ghana, February 2020: A Cross-Sectional Study.

Inappropriate use of antibiotics has led to the presence of antibiotic-resistant bacteria in ambient air. There is no published information about the presence and resistance profiles of bacteria in ambient air in Ghana. We evaluated the presence and antibiotic resistance profiles of selected bacterial, environmental and meteorological characteristics and airborne bacterial counts in 12 active air quality monitoring sites (seven roadside, two industrial and three residential) in Accra in February 2020. Roadside sites had the highest median temperature, relative humidity, wind speed and PM10 concentrations, and median airborne bacterial counts in roadside sites (115,000 CFU/m3) were higher compared with industrial (35,150 CFU/m3) and residential sites (1210 CFU/m3). Bacillus species were isolated in all samples and none were antibiotic resistant. There were, however, Pseudomonas aeruginosa, Escherichia coli, Pseudomonas species, non-hemolytic Streptococci, Coliforms and Staphylococci species, of which six (50%) showed mono-resistance or multidrug resistance to four antibiotics (penicillin, ampicillin, ciprofloxacin and ceftriaxone). There was a positive correlation between PM10 concentrations and airborne bacterial counts (rs = 0.72), but no correlations were found between PM10 concentrations and the pathogenic bacteria nor their antibiotic resistance. We call for the expansion of surveillance of ambient air to other cities of Ghana to obtain nationally representative information.

The impacts of transported wildfire smoke aerosols on surface air quality in New York State: A multi-year study using machine learning

Smoke aerosols emitted from wildfires can transport across long distances and affect the local air quality in downwind regions. In New York State (NYS), the local air quality has significantly improved due to reductions in anthropogenic emission over the past decades. As the intensity and frequency of wildfires are continuously increasing under changing climate, smoke aerosols are predicted to become the dominant source of fine particulate matter (PM2.5) concentration in NYS in the future. In this study, smoke and non-smoke cases in NYS during the summer seasons of 2012–2019 were identified using satellite measurements and aerosol reanalysis products. Overall, smoke cases showed higher PM2.5 concentrations than non-smoke cases with average PM2.5 concentrations of 11.5 ± 5.9 μg m−3 and 6.6 ± 4.6 μg m−3, respectively. PM2.5 concentrations exceeding 20 μg m−3 mainly occurred during smoke cases. In addition, an artificial neural network (ANN) algorithm was used to estimate surface PM2.5 mass concentrations at 21 air quality monitoring sites in NYS. Results showed that, for smoke cases, the application of predictors designed as indicators of vertical transport mechanisms and smoke inflow from the fire source regions generally improved the model performance by reducing the model errors. Also, analysis of the variable correlations and variable importance indicated that synoptic subsidence, entrainment process, and turbulent mixing within PBL collectively contributed to PM2.5 concentrations for smoke cases. Machine learning techniques showed the capabilities of learning the general air quality features, characterizing the key contributors to PM2.5 concentrations, and distinguishing the vertical transport processes of smoke aerosols.

Outdoor air pollution and diminished ovarian reserve among infertile Korean women

BackgroundMounting evidence implicates an association between ambient air pollution and impaired reproductive potential of human. Our study aimed to assess the association between air pollution and ovarian reserve in young, infertile women.MethodsOur study included 2276 Korean women who attended a single fertility center in 2016–2018. Women’s exposure to air pollution was assessed using concentrations of particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) that had been collected at 269 air quality monitoring sites. Exposure estimates were computed for 1, 3, 6, and 12 months prior to the ovarian reserve tests. Anti-Müllerian hormone (AMH) ratio (defined as an observed-to-expected AMH based on age) and low AMH (defined as < 0.5 ng/mL) were employed as indicators of ovarian reserve. We included a clustering effect of 177 districts in generalized estimating equations approach. A secondary analysis was conducted restricting the analyses to Seoul residents to examine the association in highly urbanized setting.ResultsThe mean age was 36.6 ± 4.2 years and AMH level was 3.3 ± 3.1 ng/mL in the study population. Average AMH ratio was 0.8 ± 0.7 and low AMH was observed in 10.3% of women (n=235). The average concentration of six air pollutants was not different between the normal ovarian reserve and low AMH groups for all averaging periods. In multivariable models, an interquartile range (IQR)-increase in 1 month-average PM10 was associated with decrease in AMH ratio among total population (β= −0.06, 95% confidence interval: −0.11, 0.00). When we restrict our analysis to those living in Seoul, IQR-increases in 1 and 12 month-average PM2.5 were associated with 3% (95% CI: −0.07, 0.00) and 10% (95% CI: −0.18, −0.01) decrease in AMH ratio. The ORs per IQR increase in the six air pollutants were close to null in total population and Seoul residents.ConclusionsIn a cohort of infertile Korean women, there was a suggestive evidence of the negative association between ambient PM concentration and ovarian reserve, highlighting the potential adverse impact of air pollution on women’s fertility.

Monitoring ambient air quality in Indian city airsheds

An understanding of sources contributing to a city’s air pollution problem is crucial for building an effective clean air action plan. All the non-attainment cities under the National Clean Air Programme (NCAP) are planning or conducting a source apportionment study. Irrespective of the approaches and equipment selected to conduct these studies, the initiation process requires an understanding of the pollution loads, mix of sources, and geography of the city to decide how much to monitor for better spatial representation and how many times to monitor for better temporal representation. In this report, we defined (a) The size of NCAP city airsheds (b) The recommended number of ambient air quality monitoring sites in an airshed (c) The operational sampling frequency to support receptor-based source apportionment studies. These resources are necessary for strengthening the monitoring needs of an airshed to track pollution levels, to conduct receptor-model-based source apportionment studies, and to support long-term air quality management plans. Together, 122 NCAP cities were clubbed into 94 airsheds representing a total of 154 cities and a total population of 290.6 million - 66 airsheds contain only one city; 16 airsheds contain two cities, nine airsheds contain three cities and four airsheds (Chandigarh, Delhi, Mumbai, and Kolkata) contain 11, 7, 7, and 4 cities respectively. For the 94 airsheds, a total of 1700 sampling sites are recommended to measure and analyse particulate matter, at an average of 18 per airshed. The Tier-1 cities on the NCAP list - Ahmedabad, Bengaluru, Delhi, Hyderabad, Kolkata, Mumbai, and Pune require at least 33, 41, 78, 36, 70, 68, and 32 sampling sites, respectively, which is more than the current operational monitoring capacity in the cities.