Air Quality Stations Research Articles

To what extent is the description of streets important in estimating local air quality: a case study over Paris

Abstract. Modeling atmospheric composition at street level is challenging because pollutant concentrations within street canyons depend on both local emissions and the transport of polluted air masses from remote areas. Therefore, regional-scale modeling and local applications must be combined to provide accurate simulations of the atmospheric composition at street locations. In our study, we compare two strategies: (i) a subgrid-scale approach embedded in the chemistry–transport model (denoted Subgrid) and (ii) the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways). In both cases, the regional-scale chemistry–transport model CHIMERE provides the urban background concentrations, and the meteorological model Weather Research and Forecasting (WRF), coupled with CHIMERE, is used to provide meteorological fields. Simulation results for NOx, NO2, and PM2.5 concentrations over the city of Paris from both modeling approaches are compared with in situ measurements from traffic air quality stations. At stations located in downtown areas, with low traffic emissions, the street-network model MUNICH exhibits superior performance compared to the Subgrid approach for NOx concentrations, while comparable results are obtained for NO2. However, significant discrepancies between the two methods are observed for all analyzed pollutants at stations heavily influenced by road traffic. These stations are typically located near highways, where the difference between the two approaches can reach 58 %. The ability of the Subgrid approach to estimate accurate emission data is limited, leading to potential underestimation or overestimation of gas and fine-particle concentrations based on the emission heterogeneity it handles. The performance of MUNICH appears to be highly sensitive to the friction velocity, a parameter influenced by the anthropogenic heat flux used in the WRF model. Street dimensions do contribute to the performance disparities observed between the two approaches, yet emissions remain the predominant factor.

POTENCIAL DE FORMAÇÃO DE OZÔNIO NA HOMOLOGAÇÃO VEICULAR NO BAIRRO DE BANGU, RIO DE JANEIRO

OZONE FORMING POTENTIAL FOR VEHICULAR HOMOLOGATION IN THE NEIGHBORHOOD OF BANGU, RIO DE JANEIRO. To control vehicle emissions, the new homologation process must be reported according to the California Maximum Incremental Reactivity (MIR) scale’s procedure to calculate the ozone forming potential (OFP) of each new vehicle. This work calculates an MIR scale suitable for the Brazilian reality. Meteorological and hourly pollutant data from the Bangu Automatic Air Quality Station was inventoried and processed from March to September 2020 to adjust OZIPR Lagrangean model and obtain the MIR scale by making small increments in the concentrations of each volatile organic compounds (VOC) and calculate the ozone increase/decrease. It was found that the VOC that had the greatest impact on ozone formation was 1,2,4-trimethylbenzene, with 2.06 ppb O3 ppbC–1 VOC. The OFP was calculated based on the VOC emissions of a vehicle and it was found a value of 9.421 mg O3 km–1, a much lower value when compared using the California MIR scale of 136.608 mg O3 km–1. It is concluded that meteorological conditions and VOC speciation data in the air mixture in the studied region directly affect the photochemical reactions of ozone formation.

Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding the spatiotemporal variations of PM10 is essential for developing effective control strategies. This study aimed to enhance PM10 prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM10 concentrations, and analyzing their correlations with PM10 air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM10 air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM10 prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM10 concentration. The SHDI was negatively correlated with PM10 levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM10 predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM10 sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM10 pollution.

Modeling nonstationary surface‐level ozone extremes through the lens of US air quality standards: A Bayesian hierarchical approach

AbstractSurface‐level ozone (O) is a harmful air pollutant whose effects may be more deleterious when at its most extreme levels. Current US air quality standards are written in terms of the 3‐year average of the 4th highest annual daily maximum 8‐h O values; therefore, developing approaches based on extreme value theory may be useful. We develop a Bayesian hierarchical approach, where the ‐largest order statistics are parametrized by the generalized extreme value (GEV) distribution, while a Gaussian process is employed to characterize how the GEV parameters depend on the O precursors, namely nitrous oxides (NO) and volatile organic compounds (VOCs). The fitted model is then used to characterize the upper tail of the distribution of O and estimate O noncompliance probabilities. We illustrate the proposed method using data from an air quality station in Providence, Rhode Island (RI). The results suggest that the far upper tail of extreme O values is likely bounded, and the dependence of the upper tail distribution on NO and O is highly nonlinear, consistent with the known relationship, albeit not specifically for extreme values, in the existing scientific literature. A convolution‐based approach is used to estimate noncompliance probabilities for several covariate scenarios. Our results indicate that estimated noncompliance probabilities in recent years are much lower than in the mid‐1990s, primarily due to lower O precursor levels. However, the estimated noncompliance probabilities appear to rise sharply for hypothetical stricter O standards, even for the conditions observed in recent years.

Acute effects of ambient nitrogen dioxide pollution on outpatient visits for neurological diseases in Xinxiang, China

BackgroundAccumulating evidence suggests that exposure to air pollution acts as a potential trigger for neurological diseases (NDs), yet the current knowledge regarding the impact of ambient nitrogen dioxide (NO2) on the patients with NDs remains limited. In this study, we conducted a time-series study to evaluate the association between short-term exposure to NO2 and hospital visits for NDs in Xinxiang, China.MethodsAn over-dispersed Poisson generalized additive model was used to analyze the association between ambient NO2 concentrations and daily outpatient visits for NDs from January 1, 2015 to December 31, 2017. The model adjusted for meteorological factors, temporal trends, day of the week, and public holidays. The concentrations of air pollutants were collected from four air quality stations in Xinxiang.ResultsA total of 38, 865 outpatient visits for NDs were retrieved during the study period. 86.5% of the patients were below the age of 65 years. It was revealed that a 10 µg/m3 increase in NO2 at lag 0 was associated with a significant rise of 1.50% (95% CI: 0.45–2.56%) in outpatient visits for NDs, which was stronger during the cold season. However, the overall results from stratified analyses did not reach statistical significance.ConclusionsShort-term exposure to NO2 is associated with increased outpatient visits for NDs. These findings underscore the need for implementing mitigating measures to reduce the neurological health effects of air pollutants.

The Impact of the New Year Celebration on the Air-Pollution in Slovakia

Fireworks and pyrotechnics are an integral part of the New Year celebration that take place both in cities and rural areas as well. Depending on the meteorological conditions, these activities can raise air pollutant concentrations in ambient atmosphere. In order to estimate the size of this increase, air pollutant concentrations from up to 61 air quality stations situated in the Slovak Republic in period from 2010 to 2023 are analysed. Pollutant concentrations of PM10, PM2.5, CO, NO2, SO2, O3 and benzene from New Year's Eve evening to New Year's morning are compared with data from 25 December to 7 January. In case of PM10 557 cases were analysed. Among them in 64 % the significant exceedance in concentrations which can be attributed to the New Year celebration with high probability was observed. The statistical difference between mean PM10 concentrations during the New Year celebration hours and during the surrounding days was strong (p ≤ 0,001). For PM2.5 the situation was similar. The New Year celebration affects other pollutant concentrations studied in this work in less extend. For them it is difficult to attribute the exceedance to the fireworks. This analysis provides clear evidence of the negative impact of fireworks and pyrotechnics on the air quality, which can lead to public health risks. This work can support policy makers to implement more stringent strategies to reduce air pollution and its effects on health.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM2.5 levels.

Quantifying urban air quality through multispectral satellite imagery and Google earth Engine

The escalating concerns surrounding urban air pollution's impact on both the environment and human health have prompted increased attention from researchers, policymakers, and citizens alike. As such, this study addresses growing concerns about urban air pollution's impact on the environment and human health, emphasizing the need for early, high-resolution PM2.5 pollutant measurements. Utilizing Google Earth Engine (GEE) machine learning algorithms, our study evaluates six models over four years in Tehran and Tabriz. Inputs include satellite imagery, meteorological data, and pollutant measurements from air quality stations. Four models—Histogram Gradient Boosting, Random Forest, Extreme Gradient Boosting, and Ada Boosted Decision Trees—outperform Support Vector Machine and Linear Regression. The selected model, a combination of decision tree algorithms and Ada Boost, achieves a notable correlation coefficient of 79.8% and an RMSE of 0.271 g/m3. This superior performance enables the generation of high-resolution (30-m) PM2.5 estimates for the two cities. The study's comprehensive approach, involving various data sources and advanced machine learning techniques, contributes a valuable method for accurate PM2.5 assessment. The findings hold significance for urban air quality management and provide a potential framework for generating detailed PM2.5 datasets based on Landsat images.

O-163 The effect of ambient exposure to air pollutants on live birth rates in frozen embryo transfer cycles

Abstract Study question Do air pollutants during oocyte retrieval (OPU) and frozen embryo transfer (FET) affect in vitro fertilization (IVF) outcomes? Summary answer Environmental exposure to increased concentrations of CO either on oocyte retrieval or embryo transfer day negatively influences clinical outcomes in FET cycles. What is known already Exposure to air pollution is linked to adverse perinatal outcomes and reduced fecundity, yet its effect on fertility and IVF cycles is inconclusive. Previous studies explored associations between air pollutant concentrations and IVF outcomes with conflicting results. Nevertheless, few assessed different pollutants’ effects either on oocyte retrieval or embryo transfer days on live birth rates. While Asian and American studies suggest that pollutants affect pregnancy rates, from European studies failed to show meaningful differences in live birth rates. Considering varying pollutant levels globally, we aimed to evaluate their impact on a European population’s fertility outcomes. Study design, size, duration A retrospective cohort study analysed 1960 FET cycles conducted by a university affiliated fertility center in Europe between January 2017 to December 2022. Participants/materials, setting, methods Daily average ambient concentrations of five air pollutants (PM2.5, NO, SO2, CO, and O3) were obtained from an urban air quality monitoring station on the day of oocyte retrieval and the day of FET. Tertiles (T1, T2, T3) of each pollutant served as cut-off levels. Multiple logistic regression analysis assessed associations between pollutant exposure and pregnancy outcomes, adjusted for confounders. Main results and the role of chance Overall pregnancy and live birth rates following FET were 52.55% and 40.71%, respectively. When analysis the results according to air pollutants on the day of FET, ambient exposure to CO was significantly associated with up to 36% lower live birth (T2, OR: 0.64, 95% CI: 0.49—0.84; T3, OR: 0.67, 95% CI: 0.51—0.88; both compared to T1). Higher CO concentrations on the FET day were positively associated with increased miscarriage rates of up to 75% (T2, OR: 1.75, 95% CI: 1.12—2.75; T3, OR: 1.63, 95% CI: 1.04—2.56; both compared to T1). Limitations, reasons for caution The retrospective nature of the study and the omission of various potential covariates including environmental and patient factors that could affect IVF outcomes, limit our findings. Exposure assessment exclusively considered specific IVF days, and reliance on data from one air quality station reduces accuracy. Caution advised when interpreting the results. Wider implications of the findings This study highlights the potential impact of air quality on IVF cycle outcomes. Further investigations are needed to determine vulnerable IVF cycle phases and clarify mechanisms underlying fertility impairment. Trial registration number not applicable

Calibration of CAMS PM2.5 data over Hungary: a machine learning approach

Air pollution is a major environmental problem, and reliable monitoring of particulate matter (PM) concentrations is critical for assessing its impact on human health and the environment. The Copernicus Atmosphere Monitoring Service (CAMS) offers vital data on PM2.5 concentrations by applying a worldwide modelling system. This study compares in situ PM2.5 measurements and raw CAMS data at 0.1° × 0.1° resolutions for 2019 and 2020 in Hungary. It proposes a calibration method to improve the accuracy of CAMS PM2.5 data at the scale of air monitoring stations. In the study, the accuracy of the raw CAMS PM2.5 data is assessed based on the chosen air quality stations. Then, to improve the precision, we employed machine learning algorithms (LightGBM, Random Forest (RF), and Multiple Linear Regression (MLR)) for calibration. Initial assessment of the raw CAMS PM2.5 data showed positive hourly Spearman correlation coefficient values (SR between 0.64 and 0.87 for the 14 air quality stations used), indicating a positive relationship between the datasets but a systemic underestimation. Our findings highlight LightGBM as the most effective method, consistently demonstrating elevated correlation SR and coefficient of determination R2 values reaching up to 0.95 and 0.93, respectively, and very good RSR (Root mean square error ratio) and NSE (Nash-Sutcliffe Efficiency) values (lower than 0.5 and higher than 0.75 for RSR and NSE, respectively). In contrast, RF yields mixed results, and MLR exhibits variable performance. By correcting underestimation and lowering modelling biases, the calibrated PM2.5 data better matches ground-based observations, which can be promising for using the obtained model for accurate estimation at individual air monitoring stations.

Field comparison of dual- and single-spot Aethalometers: equivalent black carbon, light absorption, Ångström exponent and secondary brown carbon estimations

Abstract. The Aethalometer is a widely used instrument for black carbon (BC) mass concentration and light absorption coefficient (babs) measurements around the world. However, field intercomparison of the two popular models, dual-spot (AE33) and single-spot (AE31) Aethalometers, remains limited; in addition, the difference in secondary brown carbon (BrCsec) light absorption estimation between the two models is largely unknown. We performed full-year collocated AE33 and AE31 measurements in a megacity in southern China – Guangzhou. The babs values agree well between the two Aethalometers (R2 > 0.95), with AE33 / AE31 slopes ranging from 0.87 to 1.04 for seven wavelengths. AE33 consistently exhibits lower limits of detection (LODs) than AE31 for time resolutions of 2 to 60 min. The AE33 / AE31 slope for equivalent BC (eBC) was 1.2, implying the need for site-specific post-correction. The absorption Ångström exponent (AAE) obtained from different approaches does not agree very well between the two models, with the biggest discrepancy found in AAE880/950. The estimated BrCsec light absorption at 370 nm (babs370_BrCsec) was calculated using the minimum-R-squared (MRS) method for both Aethalometers. The babs370_BrCsec comparison yields a slope of 0.78 and an R2 of 0.72 between the two models, implying a non-negligible inter-instrument difference. This study highlights the high consistency in babs but less so in AAE between AE31 and AE33 and reveals site-specific correction for eBC estimation and non-negligible difference in BrCsec estimation. The results are valuable for data continuity in long-term Aethalometer measurements when transitioning from the older (AE31) to the newer (AE33) model, as anticipated in permanent global-climate and air-quality stations.

Impacts of urban emissions and air quality in São Paulo State, Brazil.

Daily violations of air quality have an impact on urban populations and cause damage to the environment. Thus, the study evaluated the violations of the daily concentrations of SO2, NO2, and PM10, in regions of the State of São Paulo (SSP), based on the National Environment Council (CONAMA) resolution no 491/2018 and the World Health Organization (WHO - World Health Organization. (2016). Ambient air pollution: a global assessment of exposure and burden of disease.) criteria. Daily SO2, NO2, and PM10data from 6 air quality stations operated by Environmental Company of the State of São Paulo CETESB (1996-2011) were organized and submitted to quality control, with data faults (gaps) being identified. The imputation of data via spline proved satisfactory in filling in the gaps (r > 0.7 and low values of Standard Error of the Estimate (SEE) and Root Mean Square Error (RMSE). The cluster analysis (CA) applied to SO2 formed only one homogeneous group (G1). Contrariwise, NO2 and PM10 formed two homogeneous groups (G1 and G2) each. The stations that showed the greatest similarity according to the CA were Cerqueira Cesar and Osasco. The cophenetic matrix generated for SO2 (0.83), NO2 (0.79), and PM10 (0.77) indicate a satisfactory adjustment of the dendrograms. The exploratory statistics applied to groups G1 and G2 point to the high variability of outliers. The WHO criteria are more restrictive than CONAMA regarding daily violations, with a reduction in SO2 and an increase in specific years for NO2 and PM10. Such variability is due to the adoption of public policies by the SSP and the influence of meteorological systems, being confirmed by the Run test that indicated oscillations in the time series, mainly in PM10, and also recognized well-defined biannual cycles.

HypeAIR: A novel framework for real-time low-cost sensor calibration for air quality monitoring in smart cities

While less reliable than authorized air quality stations, low-cost sensors help monitor air quality in areas overlooked by traditional devices. A calibration process in the same environment as the sensor is crucial to enhance their accuracy. Furthermore, low-cost sensors deteriorate over time, necessitating repeated calibration for sustained performance. HypeAIR is a novel open-source framework for the management of sensor calibration in real-time. It incorporates two calibration methodologies: a combination of machine learning models (Voting Regressor and Support Vector Regression) and the Long Short-Term Memory deep learning model. To evaluate the framework, three extensive experiments were conducted over a 2-year period in the city of Modena, Italy, to monitor NO, NO2, and O3 gases. Both calibration methodologies outperform the manufacturer calibration and our baseline (i.e., a variation of the Random Forest algorithm) and maintain efficiency over time. The availability of the source code facilitates customization for monitoring additional pollutants, while shared air quality datasets ensure reproducibility.

Does urban bus route assignment improve air quality?

Worldwide, one of the most important causes of mortality is air pollution. To solve this problem, governments have implemented policies to reduce on-road and industrial emissions. In this regard, the Barcelona city council and Transports Metropolitans de Barcelona (TMB) started implementing the Nova Xarxa de Bus (NXB) to redefine the bus network following the criteria of connectivity, efficiency, and rationality. This policy was implemented in seven phases from 2012 to 2018. In this context, this paper analyses the impact of this policy on Barcelona’s air quality using a dataset from 2008 to 2016. Using a difference-in-difference approach, we show that implementing these new routes increased air quality in Barcelona. Additionally, we show that pollution decreased in each phase analysed, especially in the air quality stations near the main roads. From our results, we can infer that an optimal bus route design can improve air quality in urban areas.

Improving the quantification of fine particulates (PM2.5) concentrations in Malaysia using simplified and computationally efficient models

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2.5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2.5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16–0.94) observed in the wet and inter-monsoon seasons is due to fewer number of data used in model calibration. Integration of two AOD products from the Advanced Himawari Imager (AHI) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2.5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2.5 prediction accuracy.

Quantifying source contributions to ambient NH3 using Geo-AI with time lag and parcel tracking functions

Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3′s properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.

Assessment of Air Pollution in Ulaanbaatar Using the Moss Bag Technique.

Active moss biomonitoring, the so-called moss bag technique, widely applied in many countries, for the first time, was applied to assess the air quality in Ulaanbaatar (Mongolia). Moss bags with Sphagnum girgensohnii Russow were exposed in triplicate in three different periods: December-February, March-May, and December-May at 13 governmental air quality monitoring stations located in the vicinity of thermal power plants and residential areas. The plant tissue content of Al, Ba, Co, Cd, Cr, Cu, Fe, Mn, P, Pb, Sr, S, V, As, and Zn was determined using inductively coupled plasma-optical emission spectrometry, and a direct mercury analyzer was used to determine the Hg content. The samples in residential areas and near thermal power plants that were exposed for 3months in winter and for 6months (winter to spring) were characterized by the highest accumulation of the elements. In the moss bags exposed during spring, maximum accumulation of the determined elements was noted in residential areas and near main roads. Regardless of the exposure time and duration, the highest accumulation of Al, Fe, and V was determined at Dambadarjaa air quality station located near a highway and of Hg near the Amgalan power plant. Significant differences in element accumulation between seasons were observed, thus, the accumulation of Al, Ba, As, Co, Cr, Fe, Pb, V, and Zn was higher in spring, while P and S had higher content in the moss samples exposed during winter. The accumulation of elements over the 6-month exposure period was 1.1-6.7 times higher than that of the 3-month periods. Thus, the 6-month exposure can be considered a reliable deployment period as it ensures an adequate signal in terms of enrichment of pollutants. Factor analysis was applied to highlight the association of elements and to link them with possible sources of emission. Three factors were determined, the first one included Al, As, Ba, Co, Cr, Fe, Mn, Pb, Sr, and V and was identified as a geogenic-anthropogenic, the second (Cu, P, and S) and third (Cd and Zn) factors suggested anthropogenic origin. The Relative accumulation factor and enrichment factor were calculated to evaluate the level of air pollution and possible element sources. Considerable contributors to air pollution were Zn, Fe, As, V, Cr, and Al, which may originate from airborne soil particles of crustal matter or transport, as well as coal combustion for heating and cooking.

Application of CFD Modelling for Pollutant Dispersion at an Urban Traffic Hotspot

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NOx from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality.

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.