Air Pollution Model Research Articles

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25min and 3s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

The effect of workplace mobility on air pollution exposure inequality—a case study in the Central Belt of Scotland

A large number of epidemiological studies have identified air pollution as a major risk to human health. Exposures to the pollutants PM2.5, NO2 and O3 cause cardiovascular and respiratory diseases, cancer and premature mortality. Whilst previous studies have reported demographic inequalities in exposure, with the most deprived and susceptible often being disproportionately exposed to the highest pollutant concentrations, the vast majority of these studies have quantified exposure based only on individuals’ place of residence. Here we use anonymised personal data from UK Census 2011, and hourly modelled air pollution concentrations at 0.8 km × 1.4 km spatial resolution in the Central Belt of Scotland, to investigate how inclusion of time spent at place of work or study affects demographic inequalities in exposure. We split the population by sex, ethnic group, age and socio-economic status. Exposure gradients are observed across all demographic characteristics. Air pollution exposures of males are more affected by workplace exposures than females. The White ethnic group has the lowest exposures to NO2 and PM2.5, and highest to O3. Exposures to NO2 and PM2.5 tend to peak between the ages of 21 and 30, but those aged 31–50 tend to be most impacted by the inclusion of time spent at workplace in the exposure assessment. People in the two least deprived deciles consistently have the lowest residential-only and combined residential-workplace exposure to NO2 and PM2.5, but experience the highest increase in exposure when including workplace. Overall, including workplace exposure results in relatively small change in median exposure but attenuates some of the exposure inequalities associated with ethnicity and socioeconomic status observed in exposure assessments based only on place of residence.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- = 0.51 (with LCS). We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Associations of neighborhood greenspace, and active living environments with autism spectrum disorders: A matched case-control study in Ontario, Canada

BackgroundIncreasing evidence links early life residential exposure to natural urban environmental attributes and positive health outcomes in children. However, few studies have focused on their protective effects on the risk of autism spectrum disorder (ASD). The aim of this study was to investigate the associations of neighborhood greenspace, and active living environments during pregnancy with ASD in young children (≤6 years). MethodsWe conducted a population-based matched case-control study of singleton term births in Ontario, Canada for 2012–2016. The ASD and environmental data was generated using the Ontario Autism Spectrum Profile, the Better Outcomes Registry & Network Ontario, and Canadian Urban Environmental Health Research Consortium. We employed conditional logistic regressions to estimate the odds ratio (OR) between ASD and environmental factors characterizing selected greenspace metrics and neighborhoods conducive to active living (i.e., green view index (GVI), normalized difference vegetation index (NDVI), tree canopy, park proximity and active living environments index (ALE)). ResultsWe linked 8643 mother-child pairs, including 1554 cases (18%). NDVI (OR 1.034, 0.944–1.024, per Inter Quartile Range [IQR] = 0.08), GVI (OR 1.025, 95% CI 0.953–1.087, per IQR = 9.45%), tree canopy (OR 0.992, 95% CI 0.903–1.089, per IQR = 6.24%) and the different categories of ALE were not associated with ASD in adjusted models for air pollution. In contrast, living closer to a park was protective (OR 0.888, 0.833–0.948, per 0.06 increase in park proximity index), when adjusted for air pollution. ConclusionsThis study reported mixed findings showing both null and beneficial effects of green spaces and active living environments on ASD. Further investigations are warranted to elucidate the role of exposure to greenspaces and active living environments on the development of ASD.

Route selection of periodic multimodal transport for logistics company: An optimisation approach

For transporting cargo from an origin to a destination, there are several routes with various transport modes, but in practice, only one route should be selected. In this regard, one of the main activities of Logistics Companies (LCs) is to select an optimal route for transporting freight. This complexity intensifies in periodic transport across multiple origins and destinations. This paper aims to develop a dual-phase mathematical model for LCs, considering cost, time, reliability, and air and noise pollution. Indeed, in periodic transport, there is a possibility of merging the cargo with the next shipment, which can lead to lower total costs. Multimodality is also another important consideration for LCs that can reduce cost and environmental impacts. The model leverages Data Envelopment Analysis in the tactical planning phase to filter efficient routes and a multi-objective model in the operational phase for optimal route selection. The proposed approach is validated by the real case of Thailand and Vietnam, which presents practical and managing insights for LCs and shippers. The study underscores the significance of route adjustments in periodic transport and provides valuable insights for LCs and shippers, emphasising the benefits of multimodal solutions in optimising freight logistics.

419 Synergistic White Matter Injury Secondary to Chronic Cerebral Hypoperfusion and Air Pollution is Reduced by Microglia-Specific TLR4-Knockdown

INTRODUCTION: Air pollution is recognized as a neurotoxin which damages white matter and increases the risk of cognitive decline. Our previous work in a murine model found that air pollution in the setting of chronic cerebral hypoperfusion (CCH) leads to a synergistic deleterious effect on white matter. Air pollution studies have shown that increased microglial activation was associated with neurotoxicity. We hypothesize that inhibition of a microglial-specific TLR4 pathway will decrease the neurodegenerative effects that normally result from combined CCH and air pollution. METHODS: Our air pollution model used eight weeks of standardized Diesel Exhaust Particles (DEP) from National Institute of Science and Technology (DEP NIST2975). We performed bilateral carotid stenosis (BCAS) surgery to induce CCH using Wild Type (WT) and TLR4 flx/flx (CX3CR1CreER +/-) (TLR4-KD) in 2-month-old male mice. WT and TLR4-KD mice were randomized into eight groups as follows: WT-filtered air (FA), WT-DEP, WT-FA-BCAS, WT-DEP-BCAS, TLR4-KD-FA, TLR4-KD-DEP, TLR4-KD-FA-BCAS, and TLR4-KD-DEP-BCAS. Neurotoxicity was quantified with immunohistochemistry of degraded myelin basic protein (dMBP), ionized calcium-binding adaptor molecule-1 (Iba-1), and complement (C5a). RESULTS: DEP exposure resulted in increased white matter injury in corpus callosum (dMBP), worsened by the presence of CCH (p = 0.004) and significantly attenuated by TLR4-KD (p = 0.005). DEP exposure resulted in increased white matter microglial activation (Iba-1), worsened by the presence of CCH (p < 0.0001) and significantly attenuated by TLR4-KD (p = 0.04).DEP exposure resulted in increased white matter complement activation (C5a), worsened by the presence of CCH (p < 0.0001) and significantly attenuated by TLR4-KD (p < 0.0001). CONCLUSIONS: Microglial-specific TLR4-KD reduces air pollution-induced neurotoxicity measured by white matter injury, microglial activation, and neuroinflammation.

Air pollutant prediction model based on transfer learning two-stage attention mechanism

Atmospheric pollution significantly impacts the regional economy and human health, and its prediction has been increasingly emphasized. The performance of traditional prediction methods is limited due to the lack of historical data support in new atmospheric monitoring sites. Therefore, this paper proposes a two-stage attention mechanism model based on transfer learning (TL-AdaBiGRU). First, the first stage of the model utilizes a temporal distribution characterization algorithm to segment the air pollutant sequences into periods. It introduces a temporal attention mechanism to assign self-learning weights to the period segments in order to filter out essential period features. Then, in the second stage of the model, a multi-head external attention mechanism is introduced to mine the network's hidden layer key features. Finally, the adequate knowledge learned by the model at the source domain site is migrated to the new site to improve the prediction capability of the new site. The results show that (1) the model is modeled from the data distribution perspective, and the critical information within the sequence of periodic segments is mined in depth. (2) The model employs a unique two-stage attention mechanism to capture complex nonlinear relationships in air pollutant data. (3) Compared with the existing models, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the model decreased by 14%, 13%, and 4%, respectively, and the prediction accuracy was greatly improved.

Spatial modeling of air pollution in Kharkiv city


 
 
 One of the most pressing problems facing cities, which negatively affects public health. There is a global practice of online monitoring of the content of pollutants in the air of settlements and informing the population about dangerous periods of increased pollution concentrations for certain categories of diseases and the population as a whole. It is also likely that one of the factors of pollution spread is the landscape differentiation of the city’s territory. The purpose of the study presented in this article is to develop statistical and cartographic models of the spatial distribution of air pollutants within the city of Kharkiv and to establish the relationship between the degree of air pollution and types of urban landscapes. Methods. Based on the analysis of the main methodological approaches and tasks of cartographic modeling of the spatial distribution of air pollution, the study substantiates the choice of the modeling method in this study; the features of a large city as an object of cartographic modeling of air pollution are assessed. Results. Based on the results of long-term monitoring by the Kharkiv Regional Center for Hydrometeorology, a statistical analysis of the results of the maximum and average content of pollutants in the atmosphere of Kharkiv was made and the spatial distribution of air pollution in Kharkiv (including (dust, carbon monoxide, nitrogen dioxide, sulfur dioxide, formaldehyde) was modeled, and patterns of pollution distribution were identified according to landscape types. Conclusions. In general, the spatial analysis of pollutant concentrations in the territories of Kharkiv showed that, in terms of maximum concentrations, the southeastern part of Kharkiv is most affected by carbon monoxide, nitrogen dioxide, and formaldehyde. The reason for this is the large area of industrial facilities. The northwestern part of the city has the highest concentrations of dust and sulfur dioxide. There is a lowering of the relief in the floodplain type of landscape and dense development. Intensive traffic and lowered relief (floodplain landscape) cause high average concentrations of dust, carbon monoxide, and sulfur dioxide in the southern part of the city. Nitrogen dioxide concentrations exceed the TLK in the northwest. The reason is the lowering of the relief on the outskirts of the city (floodplain landscape). The central part of the city has a high average concentration of formaldehyde. In general, a pattern of high concentrations of pollutants was found on the floodplains of the Lopan and Kharkiv rivers, with the lowest concentrations confined to watershed types of terrain between the Kharkiv and Lopan rivers and the Udy and Lopan rivers.
 
 

The Danish Lagrangian Model (DALM): Development of a new local-scale high-resolution air pollution model

This paper describes the DAnish Lagrangian Model (DALM), which is a new high-resolution air pollution model based on the concept of Lagrangian particles. The new model is developed from the simpler Gaussian plume-in-grid Urban Background Model (UBM) and is to be integrated with the DEHM/UBM/AirGIS modeling system, developed at Aarhus University. In the first part of the paper, the theoretical foundation of the model is presented, and the implementation of a large set of physical and numerical parameterizations is discussed. The second part describes the validation of DALM against measurements, applying different combinations of the implemented parameterizations. This validation demonstrates that DALM can accurately reproduce spatiotemporal patterns in the measured data and that its performance is more sensitive to parameterizations of vertical compared to horizontal transport. Conclusively, the combination of parameterizations yielding the best model performance is determined based on a ranking system, and future improvements to DALM are outlined.

Air Pollution and Primary DNA Damage among Zagreb (Croatia) Residents: A Cross-Sectional Study.

More than eight million premature deaths annually can be attributed to air pollution, with 99% of the world's population residing in areas below recommended air quality standards. Hence, the present study aimed to examine the association between primary DNA damage and air pollution data among 123 participants enrolled between 2011 and 2015 in Zagreb, Croatia. While most measured air pollutants adhered to regulatory limits, benzo[a]pyrene concentrations bound to PM10 exceeded them. Factorial analysis narrowed down air pollution data to four exposure factors (particulate matter, two metal factors, and other pollutants). Despite the absence of significant positive associations between modeled air pollution exposure factors and comet assay descriptors (tail length, tail intensity, tail moment, and highly damaged nuclei), the critical health implications of air pollution warrant further investigations, particularly with biomarkers of exposure and different biomarkers of effect in populations facing air pollution exposure.

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ПАРАЛЕЛЬНОЇ ОБЧИСЛЮВАЛЬНОЇ СХЕМИ ІДЕНТИФІКАЦІЇ ІНТЕРВАЛЬНИХ ДИСКРЕТНИХ МОДЕЛЕЙ НА ОСНОВІ РОЙОВОГО ІНТЕЛЕКТУ

The research addressed in the paper focuses on organizing computations to solve NP-complex problems. Specifically, it examines the task of parametric identification of discrete models with distributed parameters based on the analysis of interval data. Computational algorithms inspired by the behavioral models of honeybee colonies are proposed to address this task. The application of the proposed algorithm enhances the efficiency of solving parametric identification problems for interval discrete models with distributed parameters, albeit with high computational time complexity. Therefore, the study suggests using parallel computing algorithms to reduce the time complexity. To assess the effectiveness of parallel computing in identifying interval discrete models with distributed parameters, computational experiments are proposed using examples of modeling air distribution and pollution by automotive exhaust emissions. The computational experiments, based on behavioral models of honeybee colonies on a four-core processor, demonstrate increased efficiency in all experiments, with higher task complexity leading to greater parallelization efficiency. However, it is noted that the average number of generations required for the parallel algorithm to find a solution is significantly higher in some experiments compared to the sequential algorithm. This sensitivity indicates that the algorithm is highly influenced by the initially generated points in the solution search space. Overall, the study establishes the feasibility of parallelizing the computational scheme for solving parametric identification problems on other promising parallel architectures, such as graphical processors.

The Impact of Environmental Benzene, Toluene, Ethylbenzene, and Xylene Exposure on Blood-Based DNA Methylation Profiles in Pregnant African American Women from Detroit.

African American women in the United States have a high risk of adverse pregnancy outcomes. DNA methylation is a potential mechanism by which exposure to BTEX (benzene, toluene, ethylbenzene, and xylenes) may cause adverse pregnancy outcomes. Data are from the Maternal Stress Study, which recruited African American women in the second trimester of pregnancy from February 2009 to June 2010. DNA methylation was measured in archived DNA from venous blood collected in the second trimester. Trimester-specific exposure to airshed BTEX was estimated using maternal self-reported addresses and geospatial models of ambient air pollution developed as part of the Geospatial Determinants of Health Outcomes Consortium. Among the 64 women with exposure and outcome data available, 46 differentially methylated regions (DMRs) were associated with BTEX exposure (FDR adjusted p-value < 0.05) using a DMR-based epigenome-wide association study approach. Overall, 89% of DMRs consistently exhibited hypomethylation with increasing BTEX exposure. Biological pathway analysis identified 11 enriched pathways, with the top 3 involving gamma-aminobutyric acid receptor signaling, oxytocin in brain signaling, and the gustation pathway. These findings highlight the potential impact of BTEX on DNA methylation in pregnant women.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining.

Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 μg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 μg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.

Electrification of Transit Buses in the United States Reduces Greenhouse Gas Emissions.

The transportation sector is the largest emitter of greenhouse gas emissions (GHGs) in the United States. Increased use of public transit and electrification of public transit could help reduce these emissions. The electrification of public transit systems could also reduce air pollutant emissions in densely populated areas, where air pollution disproportionally burdens vulnerable communities with high health impacts and associated social costs. We analyze the life cycle emissions of transit buses powered by electricity, diesel, gasoline, and compressed natural gas and model GHGs and air pollutants mitigated for a transition to a fully electric U.S. public transit bus fleet using transit agency-level data. The electrification of the U.S. bus fleet would reduce several conventional air pollutants and has the potential to reduce transit bus GHGs by 33-65% within the next 14 years depending on how quickly the transition is made and how quickly the electricity grid decarbonizes. A levelized cost of driving analysis shows that with falling capital costs and an increase in annual passenger-kilometers of battery electric buses, the technology could reach levelized cost parity with diesel buses when electric bus capital costs fall below about $670 000 per bus.

Air pollution removal with urban greenery – Introducing the Vegetation Impact Dynamic Assessment model (VIDA)

Urban greenery is identified as a potential tool in air pollution mitigation. However, the impact is still debated. This paper introduces the innovative VIDA (Vegetation Impact Dynamic Assessment) model, specifically designed to quantify air pollution removal through deposition on vegetation. The VIDA model offers an advanced representation of vegetation that could be integrated into urban air quality dispersion models in the future. Furthermore, the model serves as a valuable tool for exploring the intricate interactions among deposition, resuspension, and wash-off processes, as well as understanding how meteorological conditions and various leaf traits influence these processes.The current version of the model focuses on particulate matter (PM) and encompasses a range of processes, including deposition on vegetation surfaces, encapsulation within the waxy cuticle, wind-driven resuspension, and wash-off. Additionally, the model takes into consideration dynamic changes in PM concentrations on the leaf surface over time, incorporating factors such as PM size fractions, meteorological conditions, and leaf characteristics. This comprehensive approach allows for the evaluation of various species or species groups based on their distinct traits. The VIDA model effectively reproduces measured data, yet continued evaluation remains crucial as new data emerges. Notably, challenges were encountered due to data scarcity and the absence of standardized methods for characterizing vegetation traits. Addressing these challenges and refining the representation of wash-off process will enhance the VIDA model's utility in predicting the dynamic relationship between vegetation and air quality.The introduction of VIDA provides a significant advancement in modelling air pollution removal by deposition to vegetation at a relevant local scale and enables inclusion of urban greenery as tool in urban planning for air pollution mitigation.

Modeling of Air Pollution in the City Using Thematic Diagrams

The proposed methodology for assessing local pollution levels demonstrates the potential for addressing such tasks in principle. However, its practical implementation necessitates the con-sideration of numerous diverse factors, including unforeseen events such as accidents, com-plexities arising from military scenarios, and human factors. Furthermore, it should account for recent climatic changes characterized by rising temperatures and prolonged precipitation defi-cits, requiring adjustments to correction data for variable Di. Effective utilization of accumulat-ed data and the availability of thematic maps proves advantageous in cities with established ur-ban planning solutions and a stable transport network configuration. Successful implementation hinges on the completeness and reliability of data, accessibility to data and observational out-comes from stationary monitoring stations. Most significantly, the adoption of this metho-dology depends on practical demand and interest, the potential for automation of tasks con-ducted by specialized organizations and services, and compliance with international standards and requirements.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe.We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month.Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations.Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31–0.66 for NO2, 0.4–0.79 for O3, 0.4–0.78 for PM10, 0.46–0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6).In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Representing Ozone Formation from Volatile Chemical Products (VCP) in Carbon Bond (CB) Chemical Mechanisms

Volatile organic compound (VOC) emissions to the atmosphere cause air pollution associated with adverse health outcomes. Volatile chemical products (VCPs) have emerged as a VOC emission category that is poorly characterized by air pollution models. VCPs are present throughout developed economies in manufactured products that include paints, cleaning agents, printing inks, adhesives and pesticides. Air quality models must accurately represent the atmospheric chemistry of VCPs to develop reliable air quality plans. We develop a chemical mechanism for oxidant formation by VCP compounds that is compatible with version 6 of the Carbon Bond (CB6) mechanism. We analyzed a recent U.S. VCP emission inventory and found that ~67% of the emissions mass can be well-represented by existing CB6 mechanism species but ~33% could be better represented by adding 16 emitted VCP species including alcohols, ethers, esters, alkanes and siloxanes. For larger alkanes, an important VCP category, our mechanism explicitly represents temperature-dependent organic nitrate formation and autoxidation via 1,6 H-shift reactions consistent with current knowledge. We characterized the ozone forming potential of each added VCP species and compared it to the current practice of representing VCP species by surrogate species. Nine of the sixteen added VCP species are less reactive than the current practice, namely i-propanol, dimethyl ether, methyl formate, ethyl formate, methyl acetate, larger esters, i-butane, large alkanes and siloxanes. These less reactive VCP species are characterized by having OH-reactions that form un-reactive products. A total of 7 of the 16 VCP species are more reactive than current practice, namely n-propanol, ethylene glycol, propylene glycol, larger alcohols, diethyl ether, larger ethers and ethyl acetate. These more reactive VCP species are characterized as containing functional groups that promote faster OH-reaction. The VCP chemical mechanism for CB6 can improve how VCP impacts to oxidants are represented and will be incorporated to CB7. Changes in oxidant formation resulting from the mechanism update will depend on how VCP emissions are speciated for modeling, which is uncertain, and impacts may go in opposite directions for specific categories of VCP emissions that have unique chemical speciation characteristics. We provide guidance to help modelers implement the VCP mechanism update.

The price of clean air – quantifying air pollution exposure in real estate decisions

PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.