Ambient Monitoring Stations Research Articles

Parental stress interaction with environmental factors in childhood allergic asthma: Early life significance

Although stress and indoor/outdoor environments have been independently linked with asthma, their interactions on childhood physician-diagnosed allergic asthma (PDAA) remain undisclosed. We aimed to research the effect of parental stress and their interactions with indoor/outdoor environmental factors on childhood PDAA. A retrospective cohort study was undertaken of preschoolers at Changsha, a city in central China. We collected information on parental stress levels and various indoor environmental exposures, through questionnaire surveys. Outdoor air pollution exposure was obtained from ten ambient pollution monitoring stations in Changsha, and individual pollution exposure were calculated. Using logistic regression models, we assessed the individual and synergistic impacts of parental stress and indoor/outdoor factors on childhood PDAA. We found that preschoolers' PDAA showed a significant association with high parental gross annual income and psychological stress indicators including fatigue, headache, and inattention, with ORs = 2.10 (1.31–3.35), 1.38 (1.04–1.82), 1.33 (1.07–1.65), and 1.42 (1.14–1.76), respectively. Furthermore, in preschoolers with high parental economic stress, the association between indoor mold/moisture spots and PDAA was notably more pronounced. Significantly stronger association between outdoor SO2 exposure in the past year in preschoolers with socially stressed parents. Parental headache and inattention exacerbated the effects of PM2.5-10 and PM10 exposure during the 7-9th months before pregnancy and throughout the pregnancy on PDAA. Preschoolers with high parental psychological stress and high exposure to outdoor atmospheric pollutants showed an increased likelihood of developing PDAA. Our research suggests that both parental stress and its interaction with hazardous environments from indoor sources and atmospheric pollutants contribute to PDAA.

Impacts of anthropogenic emissions and meteorology on spring ozone differences in San Antonio, Texas between 2017 and 2021

San Antonio has been designated as ozone nonattainment under the current National Ambient Air Quality Standards (NAAQS). Ozone events in the city typically occur in two peaks, characterized by a pronounced spring peak followed by a late summer peak. Despite higher ozone levels, the spring peak has received less attention than the summer peak. To address this research gap, we used the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC) model to simulate San Antonio's ozone changes in the spring month of May from 2017 to 2021 and quantified the respective contributions from changes in anthropogenic emissions and meteorology. In addition to modeling, observations from the San Antonio Field Studies (SAFS), the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Stations (CAMS), and the spaceborne TROPOspheric Monitoring Instrument (TROPOMI) are used to examine and validate changes in ozone and precursors. Results show that the simulated daytime mean surface ozone in May 2021 is 3.8 ± 0.6 ppbv lower than in May 2017, which is slightly less than the observed average differences of −5.3 ppbv at CAMS sites. The model predicted that the anthropogenic emission-induced changes contribute to a 1.4 ± 0.5 ppbv reduction in daytime ozone levels, while the meteorology-induced changes account for a 2.4 ± 0.6 ppbv reduction over 2017–2021. This suggests that meteorology plays a relatively more important role than anthropogenic emissions in explaining the spring ozone differences between the two years. We additionally identified (1) reduced NO2 and HCHO concentrations as chemical reasons, and (2) lower temperature, higher humidity, increased wind speed, and a stronger Bermuda High as meteorological reasons for lower ozone levels in 2021 compared to 2017. The quantification of the different roles of meteorology and ozone precursor concentrations helps understand the cause and variation of ozone changes in San Antonio over recent years.

Heat Exposure among Adult Women in Rural Tamil Nadu, India.

Exposure to heat is associated with a substantial burden of disease and is an emerging issue in the context of climate change. Heat is of particular concern in India, which is one of the world's hottest countries and also most populous, where relatively little is known about personal heat exposure, particularly in rural areas. Here, we leverage data collected as part of a randomized controlled trial to describe personal temperature exposures of adult women (40-79 years of age) in rural Tamil Nadu. We also characterize measurement error in heat exposure assessment by comparing personal exposure measurements to the nearest ambient monitoring stations and to commonly used modeled temperature data products. We find that temperatures differ across individuals in the same area on the same day, sometimes by more than 5 °C within the same hour, and that some individuals experience sharp increases in heat exposure in the early morning or evening, potentially a result of cooking with solid fuels. We find somewhat stronger correlations between the personal exposure measurements and the modeled products than with ambient monitors. We did not find evidence of systematic biases, which indicates that adjusting for discrepancies between different exposure measurement methods is not straightforward.

The sensitivity of health impact assessments of PM2.5 from South African coal-fired power stations

Unabated emissions of precursor gases from coal-fired power stations in South Africa greatly contribute to ambient PM2.5 concentration in the region, but estimates of the number of associated premature mortalities vary vastly. The application of the exposure-response function is one of the reasons for the differences in estimates of premature mortalities; since the relationship between mortality and PM2.5 exposure flattens out at higher PM2.5 concentrations, a smaller number of premature mortalities is calculated if a reduction in PM2.5 concentration occurs at the actual ambient concentration (“avoidable deaths”), while a greater number of mortalities is calculated if other sources are not considered (at low ambient concentrations) (“single-source deaths”). It is more appropriate to attribute deaths to a source in proportion to the contribution made by the source to ambient PM2.5 levels (“attributable deaths”). Total ambient PM2.5 concentrations are extrapolated from 38 ambient monitoring stations in the north-eastern region of South Africa using a regression model based on household socio-economic factors, and PM2.5 concentrations derived from power station emissions are estimated using a reduced-form intake fraction model. It is estimated that there are around 800 attributable premature mortalities per year due to PM2.5 from coal-fired power station emissions in South Africa. The number of premature mortalities is 44% higher when calculating single-source deaths and 53% lower when calculating avoidable deaths. Using the attributable death approach is particularly important in highly polluted areas such as low-income residential areas where households use solid and liquid fuels.

Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas

Abstract. The TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign probed Houston air quality with a comprehensive suite of ground-based and airborne remote sensing measurements during the intensive operating period in September 2021. Two post-frontal high-ozone episodes (6–11 and 23–26 September) were recorded during the aforementioned period. In this study, we evaluated the simulation of the planetary boundary layer (PBL) height and the vertical ozone profile by a high-resolution (1.33 km) 3-D photochemical model, the Weather Research and Forecasting (WRF)-driven GEOS-Chem (WRF-GC). We evaluated the PBL heights with a ceilometer at the coastal site La Porte and the airborne High Spectral Resolution Lidar 2 (HSRL-2) flying over urban Houston and adjacent waters. Compared with the ceilometer at La Porte, the model captures the diurnal variations in the PBL heights with a very strong temporal correlation (R>0.7) and ±20 % biases. Compared with the airborne HSRL-2, the model exhibits a moderate to strong spatial correlation (R=0.26–0.68), with ±20 % biases during the noon and afternoon hours during ozone episodes. For land–water differences in PBL heights, the water has shallower PBL heights compared to land. The model predicts larger land–water differences than the observations because the model consistently underestimates the PBL heights over land compared to water. We evaluated vertical ozone distributions by comparing the model against vertical measurements from the TROPospheric OZone lidar (TROPOZ), the HSRL-2, and ozonesondes, as well as surface measurements at La Porte from a model 49i ozone analyzer and one Continuous Ambient Monitoring Station (CAMS). The model underestimates free-tropospheric ozone (2–3 km aloft) by 9 %–22 % but overestimates near-ground ozone (<50 m aloft) by 6 %-39 % during the two ozone episodes. Boundary layer ozone (0.5–1 km aloft) is underestimated by 1 %–11 % during 8–11 September but overestimated by 0 %–7 % during 23–26 September. Based on these evaluations, we identified two model limitations, namely the single-layer PBL representation and the free-tropospheric ozone underestimation. These limitations have implications for the predictivity of ozone's vertical mixing and distribution in other models.

Using Low-Cost Sensors to Assess PM2.5 Concentrations at Four South Texan Cities on the U.S.—Mexico Border

Low-cost sensors have been used considerably to characterize air pollution in the last few years. This study involves the usage of this technology for the first time to assess PM2.5 pollution at four cities on the U.S.–Mexico border. These cities in the Lower Rio Grande Valley Region of South Texas are Brownsville, Edinburg, Weslaco, and Port Isabel. A year-long sampling campaign was undertaken from 1 March 2021 to 31 March 2022. TSI BlueSky™ Air Quality Monitors were deployed concurrently at 11 different locations in these four cities. Twenty-four-hour PM2.5 concentrations from these sensors were then compared with ambient PM2.5 data available at the Texas Commission on Environmental Quality (TCEQ) Continuous Ambient Monitoring Station (CAMS) sites to elucidate spatial and temporal variability in the pollutant concentrations at the neighborhood level. The results indicate low to moderate spatial heterogeneity in the PM2.5 concentrations throughout the region. Our findings suggest that low-cost sensors in combination with CAMS sites have the potential to aid community monitoring for real-time spatiotemporal PM2.5 pollution patterns.

Personal exposure to heat amongst older adult women in Tamil Nadu, India

Background & Aim: In 2019, 127,000 deaths were attributed to heat exposure globally, with over 40% in India, where a substantial portion of the population has limited access to cooling technologies. This substantial burden is expected to worsen with climate change, but little is known about personal exposure to outdoor temperatures in India We leverage data collected as part of the Household Air Pollution Intervention Network (HAPIN) randomized controlled trial to describe personal temperature exposure of older adult women in Tamil Nadu. We compare personal exposure to ambient monitoring stations and modelled temperature products. Methods: As part of HAPIN, older adult women in Tamil Nadu periodically wore instrumentation for a 24-hr period to measure air pollution; these instruments also measure temperature. We analyzed temperature measurements taken between 2018 and 2021 and compared them to the nearest identified ambient monitoring stations, and to the ERA5 and GLDAS meteorological products. Results: Personal measurements were recorded from 105 different participants for a total of 1.7 million datapoints. The mean temperature recorded was 28.6 °C with a standard deviation of 3.5 °C. Differences of ≥5 °C across individuals on the same day were not uncommon, nor were intra-individual exposures that varied by several degrees within an hour -- potentially a result of elevated heat while cooking. ERA5 estimates were more positively correlated with personal exposure than with GLDAS or ambient monitors. Correlations varied by season, with best performance across all products during the monsoon season. Conclusions: Accurate exposure assessment is important for assessing health effects of heat – a growing problem. There is substantial variability in how well public datasets correlate with personal experience, not only by product type but also location and season. It is important to quantify these differences to appropriately estimate health effects. Keywords: Heat, India, Remote Sensing, Exposure Assessment

Effect of lockdown amid second wave of COVID-19 on environmental noise scenario of the megacity Delhi, India.

This paper analyzes the impact of second wave of COVID-19 lockdown on environmental noise levels of 25 sites in Delhi city and compares the noise scenario during pre-lockdown, lockdown, and post-lockdown periods. The study utilized the noise monitoring data acquired from 25 real-time ambient noise monitoring stations, installed by the Delhi Pollution Control Committee, Delhi, at various sites throughout Delhi city. A significant reduction of up to 10 and 3 dB(A) in day and night equivalent noise levels, respectively, had been observed during the lockdown period as compared to the pre-lockdown and post-lockdown periods. The study also revealed that only nine sites, including four industrial and five commercial zone sites, complied with the ambient noise standards during lockdown period, and no silence or residential zone sites complied with the ambient noise standards even during the lockdown period. A roadmap for environmental noise management and control is suggested. The study also reports the community's perception toward the change in acoustic environment of Delhi city during the lockdown period by conducting an environmental noise perception survey. The present study should be helpful in devising noise control action plans and policy interventions for environmental noise management and control in the metropolitan city Delhi, India.

Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada

We evaluated the uncertainty associated with secondary pollutants formation due to different chemical mechanisms in photochemical modelling. The CMAQ modelling system was utilized in conjunction with CB6R3, SAPRC07, and RACM2 chemical mechanisms and compared the concentrations of various chemical species, including ozone (O3) and particulate matter (PM2.5). Using datasets from ambient monitoring stations, we assessed the performance of each of the mechanism in summer and winter. The concentrations of various chemical species predicted by the three mechanisms varied significantly. The differences are more evident in summer than in winter for most of the species, except for hydrogen peroxide (H2O2), methyl hydroperoxide (MEPX), and Secondary Organic Aerosol—Anthropogenic. We observed that the summer daytime O3 predictions showed reasonable peaks at the three air quality monitoring sites, but the nighttime values under-predicted for all three mechanisms. In the winter, all three mechanisms tend to under-predict O3. Differences in the mean O3 values (bias) at the different sites, for the different seasons, are consistent with corrections made to previous modelling studies that modified KZMIN. PM2.5 predictions with RACM2 were slightly better. The dominant PM2.5 species in summer were sulfate and SOA-Bio, which may be attributed to non-mobile sources in the region, while NO3 became dominant in winter due to more favorable conditions for forming this species, including lower temperatures and an elevated NH4 to SO4 ratio. We concluded that the differences in O3 and PM2.5 predictions between the three mechanisms are significant, implying that when developing strategic and management actions are based on modelling, the most appropriate mechanism should be considered.

Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks.

Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.

Personal Exposure to Black Carbon, Particulate Matter and Nitrogen Dioxide in the Paris Region Measured by Portable Sensors Worn by Volunteers.

Portable sensors have emerged as a promising solution for personal exposure (PE) measurement. For the first time in Île-de-France, PE to black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO2) was quantified based on three field campaigns involving 37 volunteers from the general public wearing the sensors all day long for a week. This successful deployment demonstrated its ability to quantify PE on a large scale, in various environments (from dense urban to suburban, indoor and outdoor) and in all seasons. The impact of the visited environments was investigated. The proximity to road traffic (for BC and NO2), as well as cooking activities and tobacco smoke (for PM), made significant contributions to total exposure (up to 34%, 26%, and 44%, respectively), even though the time spent in these environments was short. Finally, even if ambient outdoor levels played a role in PE, the prominent impact of the different environments suggests that traditional ambient monitoring stations is not a proper surrogate for PE quantification.

Comparative Assessment of Pollutant Concentrations and Meteorological Parameters from TCEQ CAMS Sites at Houston and Rio Grande Valley Regions of Texas, USA in 2016

Spatial and temporal heterogeneity in pollutant concentrations exists at the intra-urban level. In this research work, the concentrations of various pollutants and meteorological parameters are characterized between various central ambient monitoring sites at Houston, TX, and the Rio Grande Valley Regions of South Texas. Meteorological (temperature, relative humidity, wind speed and direction) and pollutant (O3, SO2, CO, NO2, and various PM species) concentrations were downloaded from the appropriate Texas Commission on Environmental Quality (TCEQ) Central Ambient Monitoring Station (CAMS) sites for the year 2016. Correlation Analyses and Coefficient of Divergence (COD) analyses suggest that statistically significant differences occur between the various TCEQ CAMS sites in the Houston Region. Findings from this study will help the various stakeholders involved in assessing the overall air pollutants exposure burden for the local populations.

Particulate matter measurement in South Africa: are the right questions being asked?

BACKGROUND AND AIM: Particulate matter (PM) is a key indicator of air pollution and a significant risk factor for adverse health effects in humans. High PM levels have been observed in industrialized and urban areas in South Africa. Concerns over the health effects of PM2.5 made the South African government to establish the national standard for PM2.5 in 2012. Since then, most studies conducted in South Africa have focused on determining the ambient concentration of PM2.5 and its health impacts. However, PM2.5 is not a self-contained pollutant but a mixture of different compounds including chemical and biological fractions. There is a dearth of studies measuring the relationship between exposure to individual pollutants and the combined effects of multipollutant in PM2.5 and adverse health outcomes in South Africa. Also, little is known about the exact constituents of PM responsible for the observed health effects in South Africa. This paper discusses current PM monitoring and assessment methods in use for air pollution studies in South Africa. METHODS: METHODS: A literature search using various search engines and (or) keywords was done. Articles selected for review were chosen following predefined criteria, to extract and analyze data. RESULTS:The results show that the biological and chemical components of inhalable and respirable PM play a significant role in the burden of health effects attributed to PM. In most studies, PM2.5 levels were measured with fixed ambient monitoring stations on the assumption that levels of PM2.5 recorded by the fixed monitoring station are representative of the total concentration of PM2.5 to which the residents within the study area are exposed. CONCLUSIONS:There is also an overall poor understanding of current novel methods of air quality monitoring and exposure assessment. Efforts geared towards the adoption of novel air quality monitoring and exposure assessment techniques should be instituted in South Africa. KEYWORDS: Particulate matter, Air monitoring, Exposure assessment, South Africa

Intra-urban variability of PM2.5 in a dense, low-income settlement on the South African Highveld

High concentrations of the ambient particulate matter remains a concern on the South African Highveld, particularly in densely populated low-income settlements. These areas have several local emission sources that contribute to poor air quality and are often located close to industrial and other urban areas. The local sources vary in magnitude, space, and time. In South Africa, little has been done to assess the impacts of spatiotemporal variability on the credibility of using isolated ambient observations for regulatory purposes. This study aims to evaluate the intra-urban variability of ambient PM2.5 concentrations in a dense, low-income community. Ambient fine particulate matter (PM2.5) in distinct microenvironments of KwaZamokuhle were simultaneously measured at 4 sites between March and June 2018. These measurements were collected using one permanent ambient monitoring station (AMS) and a temporary network of three E-BAM monitors (Site 2, Site 3, and Site 4). The daily PM2.5 concentrations at AMS, Site 2, Site 3, and Site 4 varied from 10 to 86 µg.m-3, 10 to 103 µg.m-3, 11 to 101 µg.m-3, and 9 to 113 µg.m-3, respectively. Extreme PM2.5 concentrations which exceeded the 24h PM2.5 NAAQS of 40 µg.m-3 were seen during the cold period (May and June); meanwhile, the warm period (March and April) recorded relatively lower PM2.5 episodes across different sections of KwaZamokuhle. During May-June, the highest diurnal concentrations of hourly averaged ambient PM2.5 were recorded at Site 4, in a downward sequence, followed by Site 3, Site 2, and AMS. Furthermore, the results showed that across KwaZamokuhle, Site 4 has the highest proportion of households using solid fuels for domestic purposes (cooking and heating) (55%) and the number of informal dwellings (117 households). Therefore, the study highlights the complexity of quantifying ambient air quality in an area where several local emission sources vary in space and time. Attempts to use monitoring data from a single station to assess ambient air quality, quantify human exposure, or evaluate the potential impacts of mitigation strategies in dense, low-income settlements should be done with care.

Intra-urban variability of PM2.5 in a dense, low-income settlement on the South African Highveld

High concentrations of the ambient particulate matter remains a concern on the South African Highveld, particularly in densely populated low-income settlements. These areas have several local emission sources that contribute to poor air quality and are often located close to industrial and other urban areas. The local sources vary in magnitude, space, and time. In South Africa, little has been done to assess the impacts of spatiotemporal variability on the credibility of using isolated ambient observations for regulatory purposes. This study aims to evaluate the intra-urban variability of ambient PM2.5 concentrations in a dense, low-income community. Ambient fine particulate matter (PM2.5) in distinct microenvironments of KwaZamokuhle were simultaneously measured at 4 sites between March and June 2018. These measurements were collected using one permanent ambient monitoring station (AMS) and a temporary network of three E-BAM monitors (Site 2, Site 3, and Site 4). The daily PM2.5 concentrations at AMS, Site 2, Site 3, and Site 4 varied from 10 to 86 µg.m-3, 10 to 103 µg.m-3, 11 to 101 µg.m-3, and 9 to 113 µg.m-3, respectively. Extreme PM2.5 concentrations which exceeded the 24h PM2.5 NAAQS of 40 µg.m-3 were seen during the cold period (May and June); meanwhile, the warm period (March and April) recorded relatively lower PM2.5 episodes across different sections of KwaZamokuhle. During May-June, the highest diurnal concentrations of hourly averaged ambient PM2.5 were recorded at Site 4, in a downward sequence, followed by Site 3, Site 2, and AMS. Furthermore, the results showed that across KwaZamokuhle, Site 4 has the highest proportion of households using solid fuels for domestic purposes (cooking and heating) (55%) and the number of informal dwellings (117 households). Therefore, the study highlights the complexity of quantifying ambient air quality in an area where several local emission sources vary in space and time. Attempts to use monitoring data from a single station to assess ambient air quality, quantify human exposure, or evaluate the potential impacts of mitigation strategies in dense, low-income settlements should be done with care.

Ambient air quality data reported at Sasol Secunda monitoring stations during COVID-19 lockdown – Mpumalanga, South Africa

Various local and international research has been published on the effects of COVID-19 lockdown on ambient air quality. In most cases, a reduction in ambient NOx and PM concentrations have been observed with varying changes in ambient SO2 levels. Secunda, located in the Highveld Priority Area in Mpumalanga, South Africa is known for its large industrial facilities utilising coal as primary feedstock. The towns of Secunda and eMbalenhle provide the majority of the workforce to Sasol and has therefore been the focus of this study. The ambient air quality in the Secunda region was assessed due to the changes in human behaviour during lockdown, familiarity with the Sasol facility and the strategic locations of ambient air quality stations.Results show a clear decrease in ambient CO, NO2 and PM concentrations, especially during the first two weeks of lockdown. Only subtle changes were observed for ambient H2S and SO2 pollutant concentrations at the ambient monitoring stations. An increasing trend in all ambient species was observed towards the end and post lockdown, in contrast to declining ambient temperatures with the onset of winter. This is also contrary to the reduction in emissions from the factory that conducted annual maintenance in the month following lockdown (phase shutdown). This article concludes that human behaviour has a material local ambient impact on CO, NO2 and PM pollutant species, while H2S concentration profiles are more directly related to the industrial complex’s levels of activity. Ambient SO2 trends did not show a similar correlation with the facility’s activities (as H2S), but a stronger correlation was observed with the diverse local and regional sources in close proximity to Secunda and eMbalenhle. The influence of better dispersion especially on a local scale, brought about by more effective emission heights, is considered material. Moreover, meteorological factors, on local air quality, has been shown to be a material contributor to observed ambient air quality levels in the study domain

Dust Transport from Inland Australia and Its Impact on Air Quality and Health on the Eastern Coast of Australia during the February 2019 Dust Storm

Dust storms originating from Central Australia and western New South Wales frequently cause high particle concentrations at many sites across New South Wales, both inland and along the coast. This study focussed on a dust storm event in February 2019 which affected air quality across the state as detected at many ambient monitoring stations in the Department of Planning, Industry and Environment (DPIE) air quality monitoring network. The WRF-Chem (Weather Research and Forecast Model—Chemistry) model is used to study the formation, dispersion and transport of dust across the state of New South Wales (NSW, Australia). Wildfires also happened in northern NSW at the same time of the dust storm in February 2019, and their emissions are taken into account in the WRF-Chem model by using Fire Inventory from NCAR (FINN) as emission input. The model performance is evaluated and is shown to predict fairly accurate the PM2.5 and PM10 concentration as compared to observation. The predicted PM2.5 concentration over New South Wales during 5 days from 11 to 15 February 2019 is then used to estimate the impact of the February 2019 dust storm event on three health endpoints, namely mortality, respiratory and cardiac disease hospitalisation rates. The results show that even though as the daily average of PM2.5 over some parts of the state, especially in western and north western NSW near the centre of the dust storm and wild fires, are very high (over 900 µg/m3), the population exposure is low due to the sparse population. Generally, the health impact is similar in order of magnitude to that caused by biomass burning events from wildfires or from hazardous reduction burnings (HRBs) near populous centres such as in Sydney in May 2016. One notable difference is the higher respiratory disease hospitalisation for this dust event (161) compared to the fire event (24).

The impacts of commissioning coal-fired power stations on air quality in South Africa: insights from ambient monitoring stations

The South African electricity sector is known for its heavy reliance on coal. The aim of this study is to assess the impacts of increasing SO2 and PM emissions from the three return-to-service power stations (Komati, Camden and Grootvlei), and the newly constructed Medupi power station on ambient air quality measured in the vicinities of these power stations. Trends in ambient pollution concentrations were determined using Theil-Sen analysis. The correlation between the emissions and ambient pollution concentrations at nearby monitoring stations was determined with the Spearman partial rank correlation coefficient. Lastly, compliance of ambient pollution concentrations with the South Africa National Ambient Air Quality Standards was assessed. Few statistically significant trends in ambient SO2 and PM10 concentrations are found, and there is little correlation between increasing power station emissions and ambient pollutant concentrations in the vicinity. It is only at Camden monitoring station where there are increases in PM10 concentrations from the direction of Camden power station, and at Grootvlei monitoring station where increasing SO2 concentrations are from the directions of Grootvlei and Lethabo power stations. A strong, positive correlation between power station emissions and ambient concentrations exists only for SO2 at Grootvlei monitoring station and PM10 at Medupi monitoring station (although it is likely that the correlation at Medupi is related to construction and vehicle activity, and not emissions from Medupi power station stacks). It is concluded that the establishment of monitoring stations in the vicinities of power stations is necessary but not sufficient to monitor their impact on air quality in the surrounds.

Intercomparison of Sonde, WRF/CAMx and Satellite Sounder Profile Data for the Paso Del Norte Region

The Paso Del Norte (PdN) region comprises the city of El Paso, TX, Ciudad Juarez, Mexico, and some neighboring cities in the state of New Mexico. Developing a regional weather model for this specific region has always been challenging due to its complex terrain. To obtain more accurate weather and pollution forecasting for the PdN region, the results of the downscaled WRF (Weather Research and Forecast) model were intercompared with meteorological satellite data, with ground and radiosonde dataset. In addition, it is critical to analyze the distributions of ozone concentrations to better understand atmospheric aerosol concentrations and predict them both more accurately. Hence, in this study the ozone results of CAMx (Comprehensive Air Quality Model with Extensions) were extensively intercompared with ozonesonde data. The radiosonde/ozonesonde data were obtained throughout a campaign conducted during the summer of 2017 in the PdN region. Different meteorological variables such as temperature, pressure, relative humidity, wind speed, and ozone concentrations were used for comparison at several locations in the PdN region. The TCEQ (Texas Commission of Environment Quality) data from different CAMS (Continuous Ambient Monitoring Stations) were used for ground data intercomparison with the WRF results. The meteorological satellite sounding data were retrieved using an in-house satellite antenna receiver. The results of this research paper will not only provide better pollution forecasting capability for the PdN region but also for other regions with similar topography and terrain.

Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance

In this study, we use a deep convolutional neural network (CNN) to develop a model that predicts ozone concentrations 24 h in advance. We have evaluated the model for 21 continuous ambient monitoring stations (CAMS) across Texas. The inputs for the CNN model consist of meteorology (e.g., wind field, temperature) and air pollution concentrations (NO x and ozone) from the previous day. The model is trained for predicting next-day, 24-hour ozone concentrations. We acquired meteorological and air pollution data from 2014 to 2017 from the Texas Commission on Environmental Quality (TCEQ). For 19 of the 21 stations in the study, results show that the yearly index of agreement (IOA) is above 0.85, confirming the acceptable accuracy of the CNN model. The results also show the model performed well, even for stations with varying monthly trends of ozone concentrations (specifically CAMS-012, located in El-Paso, and CAMS-013, located in Fort Worth, both with IOA=0.89). In addition, to ensure that the model was robust, we tested it on stations where fewer meteorological variables are monitored. Although these stations have fewer input features, their performance is similar to that of other stations. However, despite its success at capturing daily trends, the model mostly underpredicts the daily maximum ozone, which provides a direction for future study and improvement. As this model predicts ozone concentrations 24 h in advance with greater accuracy and computationally fewer resources, it can serve as an early warning system for individuals susceptible to ozone and those engaging in outdoor activities.