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This paper explores the enduring impact of freeway construction on urban neighborhoods and communities, focusing on Los Angeles. The legacy of freeways in Los Angeles, often and purposefully constructed through marginalized communities during the mid-20th century for the benefit of white suburbanites, is reflected in significant social, economic, and environmental symptoms today. This paper also examines de-freeway methods to reconnect these divided communities, assessing their need and effectiveness through data analysis and case studies.The case studies presented in this paper offer a diverse range of solutions. They include the capping of I-5 for Freeway Park in Seattle, the tunneling of the Central Artery for Boston's Big Dig, the activation of I-5 underpasses for Chicano Park in San Diego, and the creation of the Ricardo Lara embankment park along the I-105 freeway in Lynnwood. These examples provide valuable insights into the processes, challenges, and successes of de-freeway and community reconnection efforts, showcasing the adaptability of these solutions in different urban contexts.The paper introduces three design proposals for freeway sites across six communities in Los Angeles. These proposals are guided by urban reformation around freeways and incorporating urban design principles prioritizing social equity, environmental sustainability, and economic revitalization for the most vulnerable and impacted communities. The proposed designs aim to restore community cohesion and foster resilient urban ecosystems.This study contributes to the ongoing discourse on sustainable urban transformation and the reclamation of public spaces for community benefit by providing a comprehensive analysis of historical impacts and contemporary solutions. The paper's findings should help inform and guide urban planning and policy advocates and professionals inspired by the reconnecting communities movement, underscoring the need for innovative approaches to mending bifurcated and ecologically disadvantaged communities.

Wildfires are increasing in duration and intensity across the United States' Pacific West region, resulting in heightened particulate matter from smoke in the atmosphere. Levels of peak particulate matter are concurrent to peak visitor attendance at National Parks, given seasonal alignment with summer vacation travel and heightened forest fire conditions. Particulate matter threatens visitor health and safety and contributes to poor visibility and a deteriorated visitor experience. To assess visitation response to diminished air quality, we utilized wildfire-generated particulate matter (PM2.5) data in conjunction with monthly attendance records for three ecoregions containing eight national parks in Washington, Oregon, and California from 2009 to 2019. We analyzed daily PM2.5 levels from data gridded at the 10 km scale for National Park Service units by Level III forest ecoregions within the National Park Service's Pacific West Unit. Data were then compared to normalized monthly visitation trends for each of the ecoregions using two statistical methods Kendall's Tau and Analysis of Variance (ANOVA) with post-hoc Tukey tests. Results demonstrate that attendance at these national parks does not decrease in response to increased PM2.5 levels. Instead, we see several statistically significant increases in attendance across these ecoregions during periods of reduced air quality. Of 115 shifts between air quality categories during the busy season of July to September, there are no significant decreases in attendance as air quality worsens. These findings suggest that visitors are willing to tolerate reduced air quality compared to other factors such as temperature or precipitation. Given that park units within each ecoregion feature diverse historical contexts, varied built environments, and unique ecological systems, our discussion specifically addresses managerial concerns associated with maintained high levels of visitation during suboptimal, and potentially dangerous, conditions. There is substantial need for specific, scalable approaches to mitigate adverse health and experiential impacts as visitors are exposed to increased risks during a range of exertional activities associated with diverse settings.

Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.

To demonstrate how a mega city can lead in decarbonizing beyond legal mandates, the city of Los Angeles (LA) developed science-based, feasible pathways towards utilizing 100% renewable energy for its municipally-owned electric utility. Aside from decarbonization, renewable energy adoption can lead to co-benefits such as improving urban air quality from reductions in combustion-related emissions of oxides of nitrogen (NOx), primary fine particulate matter (PM2.5) and others. Herein, we quantify changes to air pollutant concentrations and public health from scenarios of 100% renewable electricity adoption in LA in 2045, alongside aggressive electrification of end-use sectors. Our analysis suggests that while ensuring reliable electricity supply, reductions in emissions of air pollutants associated with the 100% renewable electricity scenarios can lead to 8% citywide reductions of PM2.5 concentration while increasing ozone concentration by 5% relative to a 2012 baseline year, given identical meteorology conditions. The combination of these concentration changes could result in net monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in year 2045 in LA, results potentially replicable for other city-scale decarbonization scenarios.

[Display omitted] Climate change is increasing the frequency and intensity of heatwaves, raising concerns about their detrimental effects on air quality. However, a role for heatwave-human-environment interactions in air pollution exacerbation has not been established. In the summer of 2022, record-breaking heatwaves struck China and Europe. In this study, we use integrated observational data and machine learning to elucidate the formation mechanism underlying one of the most severe ozone pollution seasons on record in central eastern China, an area that encompasses approximately half of China's total population and sown land. Our findings reveal that the worsened ozone and nitrogen dioxide pollution resulted from a mismatch between energy demand and supply, which was driven by both heatwaves and energy policy-related factors. The observed adverse heatwave-energy-environment feedback loop highlights the need for the diversification of clean energy sources, more resilient energy structures and power policies, and further emission control to confront the escalating climate challenge in the future. [ABSTRACT FROM AUTHOR]

Model-based clustering analysis of functional data often has normality assumption. This article considers clustering non Gaussian functional data. We propose a novel non Gaussian functional mixed-effects model without the prior information and clustering number. We use transformation functions to accommodate non Gaussian functional data. Smoothing spline ANOVA and cubic B-spline approximate unknown fixed effects and random effects, respectively. A penalized likelihood is used to estimate unknown parameters, and the consistency and asymptotic normality is provided after that. We take simulations for different measurement error distribution assumptions and adopt the air quality of Italian city data. Both simulation and actual data analysis show that the proposed method performs well and has a better clustering effect. [ABSTRACT FROM AUTHOR]

The emissions from South Asia and Southeast Asia significantly impact air quality and meteorological conditions in China. However, the individual or joint contributions of anthropogenic emissions and/or biomass burning from outside China to surface particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and aerosol optical depth (AOD) over southern China have not been fully investigated. Here, five experiments were designed to investigate the impacts of these emissions in January (winter), March (pre‐monsoon), and October (post‐monsoon) for 2017. Aerosols from South Asia and Southeast Asia contributed less to southern China during winter and post‐monsoon seasons, whereas the wind patterns and emission intensity during the pre‐monsoon season were conducive to the transport of aerosols. During pre‐monsoon season, the total emissions contributed approximately 5.0 μg m−3 to surface PM2.5 in Xizang Province. Biomass burning in Southeast Asia increased PM2.5 in Yunnan Province by 37.9 μg m−3, while anthropogenic emissions increased it by 8.9 μg m−3. Transboundary aerosols can be transported to Xizang Province and Yunnan Province, primarily influencing PM2.5 below 2 km height. It mainly affected PM2.5 levels above the planetary boundary layer over southeast China. Aerosols from outside China can account for 79.5% and 54.8% AOD in Yunnan Province and southeast China, respectively. These aerosols reduced surface incident solar radiation by approximately 6%, leading to decreases in air temperature, wind speed, and boundary layer height. The findings are only applicable to the pre‐monsoon season. Plain Language Summary: Air pollution in South Asia and Southeast Asia is closely related to anthropogenic emissions and biomass burning. Under the influence of prevailing winds, these emissions can affect air quality over southern China during different seasons. This study combines ground observations, satellite data, and chemical transport models to investigate the impacts of anthropogenic emissions and/or biomass burning from South Asia and Southeast Asia on air quality and meteorological conditions over southern China in January, March, and October for 2017. Key Points: Transportation from South Asia and Southeast Asia account for up to 50% PM2.5 concentrations over Xizang Province in ChinaAerosols transported from South Asia and Southeast Asia can impact PM2.5 at heights up to 6 km over southern China in pre‐monsoon seasonImpacts on the surface solar radiation, air temperature, relative humidity, wind speed and planetary boundary layer height are quantified [ABSTRACT FROM AUTHOR]

Nighttime oxidation of monoterpenes (MT) via the nitrate radical (NO3) and ozone (O3) contributes to the formation of secondary organic aerosol (SOA). This study uses observations in Atlanta, Georgia from 2011 to 2022 to quantify trends in nighttime production of NO3 (PNO3) and O3 concentrations and compare to model outputs from the EPA's Air QUAlity TimE Series Project (EQUATES). We present urban‐suburban gradients in nighttime NO3 and O3 concentrations and quantify their fractional importance (F) for MT oxidation. Both observations and EQUATES show a decline in PNO3, with modeled PNO3 declining faster than observations. Despite decreasing PNO3, we find that NO3 continues to dominate nocturnal boundary layer (NBL) MT oxidation (FNO3 = 60%) in 2017, 2021, and 2022, which is consistent with EQUATES (FNO3 = 80%) from 2013 to 2019. This contrasts an anticipated decline in FNO3 based on prior observations in the nighttime residual layer, where O3 is the dominant oxidant. Using two case studies of heatwaves in summer 2022, we show that extreme heat events can increase NO3 concentrations and FNO3, leading to short MT lifetimes (<1 hr) and high gas‐phase organic nitrate production. Regardless of the presence of heatwaves, our findings suggest sustained organic nitrate aerosol formation in the urban SE US under declining NOx emissions, and highlight the need for improved representation of extreme heat events in chemistry‐transport models and additional observations along urban to rural gradients. Plain Language Summary: Monoterpenes are important precursors of secondary organic aerosol (SOA), which influence air quality and climate. At night, they react with the nitrate radical (NO3) and ozone (O3). Trends in these two oxidants and their role on air quality in the southeastern United Sates (SE US) is partially dictated by changes in nitrogen oxide (NOx = NO + NO2) emissions which have declined in recent years. We find that NO3 dominates present‐day monoterpene loss in the summer, and may continue to do so even as NOx concentrations decrease in the future. We show that heatwaves in the SE US further elevate both O3 and NO3 concentrations at night, with a larger relative importance for NO3. We compare observations to a regional air quality model, and find the model overpredicts both oxidant concentrations. This overprediction may impact model‐based studies of future nighttime chemistry. Key Points: Nitrate radical (NO3) continues to control nocturnal boundary layer monoterpene oxidation in the urban SE USRegional model overpredicts oxidant concentrations and fractional NO3 contribution to monoterpene lossExtreme heat events increase nighttime NO3 concentrations, monoterpene oxidation rates, and gas‐phase organic nitrate production [ABSTRACT FROM AUTHOR]

With deteriorating air pollution and climate change, generating power with cleaner technologies to meet energy demand of economic development has become a crucial policy consideration globally. Biomass power generation, despite being cleaner than coal power generation, still emits certain air pollutants that can impact ambient air quality. On the other hand, biomass power plants might mitigate air pollution by reducing open-field crop burning. With this counteracting in play, it is unclear which effect is dominant and how biomass power generation affects ambient air quality eventually. We build a unique spatially high-resolution dataset for 20-km-radius circles, considering both regions with biomass power plants and those without, to explore the impacts of biomass power generation on air quality in nearby regions. By applying panel and canonical difference-in-differences models with various model specifications and estimation strategies, we demonstrate that biomass power generation in China significantly reduces ambient annual concentrations of PM2.5, PM10, SO2, and CO. The impacts on NO2 reduction could only be observed in summer, when open-field burnings decreased substantially. The impacts on PM2.5 could be observed in both summer and fall. No seasonal pattern is observed for the reductions in PM10, SO2, and CO concentrations. [ABSTRACT FROM AUTHOR]

Secondary organic aerosol (SOA), atmospheric particulate matter formed from low-volatility products of volatile organic compound (VOC) oxidation, affects both air quality and climate. Current 3D models, however, cannot reproduce the observed variability in atmospheric organic aerosol. Because many SOA model descriptions are derived from environmental chamber experiments, our ability to represent atmospheric conditions in chambers directly affects our ability to assess the air quality and climate impacts of SOA. Here, we develop an approach that leverages global modeling and detailed mechanisms to design chamber experiments that mimic the atmospheric chemistry of organic peroxy radicals (RO2), a key intermediate in VOC oxidation. Drawing on decades of laboratory experiments, we develop a framework for quantitatively describing RO2 chemistry and show that no previous experimental approaches to studying SOA formation have accessed the relevant atmospheric RO2 fate distribution. We show proof-of-concept experiments that demonstrate how SOA experiments can access a range of atmospheric chemical environments and propose several directions for future studies. [ABSTRACT FROM AUTHOR]

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity. [ABSTRACT FROM AUTHOR]

Background: The new P-STEP® (Personalised Space Technology Exercise Platform) app is designed to bring together tailored exercise guidance and up-to-date air quality information. The app allows individuals to plan outdoor exercise walking routes while minimising their exposure to air pollution. Individuals with chronic long-term conditions, particularly respiratory and cardiovascular conditions, can use the app in order to minimise the risk of their symptoms being exacerbated by pollution, while still gaining the benefits of outdoor exercise. Methods: This study will measure the usability and acceptability of the P-STEP® app. The study will take the form of a single-arm 12-week app pilot study based in Leicestershire, United Kingdom (UK). We will recruit a maximum of 380 participants from an existing cohort study to pilot the app for 12 weeks. Questionnaire data will be collected at three timepoints, baseline, 6 weeks and 12 weeks. The primary outcome is the System Usability Scale at 12 weeks. Secondary outcomes include the User Engagement Scale Short Form, SF-12, Recent Physical Activity Questionnaire (RPAQ), bespoke, app specific usability questions, and feasibility outcomes. Additional data collected includes participant demographic information, technology self-efficacy and adverse events. Weekly anonymised usage data from the app will also be collected by the app team and analysed separately to complement the questionnaire data. Discussion: This study will help us better understand the feasibility and acceptability of using the P-STEP® in the community. The results will also help inform future studies. Ethics and dissemination: This study has received ethical approval from the South West Frenchay Research Ethics (23/SW/0060) Committee. There is no need for further approval from the Health Research Authority as the study is not taking place in the NHS. The ClinicalTrials.gov ID number is NCT05830318. [ABSTRACT FROM AUTHOR]

In Europe mainly at winter season the PM levels exceed air quality limits, which correlated with the operation of solid-fired boilers. More and more people are returning to using these devices due to energy shortage caused by the pandemic and regional conflicts. In addition, the phenomena of co-burning fuels and municipal waste in residential boilers in primarily fuel poverty households increases further the amount of pollutants in the atmosphere. This study aims to correlate the quantity and quality of air pollutants with the type of fuel (wood and wastes) burned. Combustion experiments were conducted using oak fuel mixed with three waste groups: (1) plastics (PP, HDPE, PET); (2) textiles (polyester—PES, cotton—COT); and (3) papers (cardboard—CARD, glossy coated paper—GCP, 84C/PAP). The addition of waste to wood fuel altered the morphology of emitted particles. While waste burning doesn't always increase particle quantity, it significantly raises PAH concentrations. A strong relationship exists between waste type, particle morphology, and PAH quality, where with lower molecular weight PAHs linked to tar agglomerates and higher ones to soot agglomerates with inorganic crystals. [ABSTRACT FROM AUTHOR]

With the increase in high-rise buildings in cities, public flue exhaust systems have a direct impact on residential air quality and the living environment. Although existing studies have analyzed the problems in public flue exhaust systems through computational fluid dynamics (CFD) numerical simulations and experimental tests, these studies often lack an in-depth exploration of the specific impacts of individual components in the system. To solve this problem, this study not only thoroughly analyzes the key components in the public flue system, such as branch pipes, check valves, and tee pipes, but also develops a parametric public flue simulation system software based on C# and verifies the accuracy of the simulation through experiments. The study first determines the key parameters affecting the comprehensive resistance coefficient of the branch pipe, check valve, tee pipe, and other components through CFD simulation and experimental testing. Subsequently, a visualization program is developed using the C# language, which can quickly simulate and visualize the flow changes in the flue according to different building parameters such as the number of floors, height of floors, and size of the flue. The results confirm that the program can efficiently predict the flow distribution under different design options, providing a practical tool for the optimal design and performance evaluation of public flues, which is important for improving the air quality of the living environment. [ABSTRACT FROM AUTHOR]

With rising environmental concerns, accurate air quality predictions have become paramount as they help in planning preventive measures and policies for potential health hazards and environmental problems caused by poor air quality. Most of the time, air quality data are time series data. However, due to various reasons, we often encounter missing values in datasets collected during data preparation and aggregation steps. The inability to analyze and handle missing data will significantly hinder the data analysis process. To address this issue, this paper offers an extensive review of air quality prediction and missing data imputation techniques for time series, particularly in relation to environmental challenges. In addition, we empirically assess eight imputation methods, including mean, median, kNNI, MICE, SAITS, BRITS, MRNN, and Transformer, to scrutinize their impact on air quality data. The evaluation is conducted using diverse air quality datasets gathered from numerous cities globally. Based on these evaluations, we offer practical recommendations for practitioners dealing with missing data in time series scenarios for environmental data. [ABSTRACT FROM AUTHOR]

Five decades of monitoring data (1974–2022) at the acidified forested catchment of Langtjern in southern Norway document strong chemical recovery and browning of surface water, related to changes in sulfur (S) deposition. Further recovery is likely to be impacted by future air quality and climate, through catchment processes sensitive to climate change, where the relative importance of these drivers of recovery is poorly known. Here, we explore the importance of the aforementioned drivers for recovery using the well‐established process‐oriented Model of Acidification of Groundwater In Catchments (MAGIC) with historical and projected deposition and climate from 1860 to 2100. New in MAGIC are (i) a solubility control of dissolved organic carbon (DOC) from S deposition, which allows inclusion of the role of organic acids in chemical recovery and (ii) climate‐dependency of weathering rates. MAGIC successfully described observed chemical recovery and browning, and the change toward organic acid dominated acidification status. Hindcasts of pH predicted lower preindustrial pH than previously modeled with MAGIC (simulated without S‐dependency of DOC solubility). Future deposition resulted in limited further recovery. Climate scenarios indicated a substantially wetter future, leading to increased base cation losses and slight surface water reacidification. A sensitivity analysis revealed that a 25%–50% increase of weathering rates was needed to reach preindustrial acid‐neutralizing capacity in 2100, provided S deposition is reduced to a minimum. We predict that the limited chemical recovery from reduced S deposition will be counteracted by climate‐driven reacidification from base cation losses, but that enhanced weathering rates could partly compensate these losses. [ABSTRACT FROM AUTHOR]

We implemented a crossover study design exposing 15 participants to two indoor air quality conditions in the Well Living Lab. The first condition, the Standard Control Condition, resembled the ventilation and air supply of a typical home in the USA with a manually operated stove hood. The second condition, Advanced Control, had an automated: (i) stove hood, (ii) two portable air cleaners (PAC), and (iii) bathroom exhaust. The PM2.5 sensors were placed in the kitchen, living room, bedroom, and bathroom. Once the sensor detected a PM2.5 level of 15 μg/m3 or higher, an air quality intervention (stove hood, PAC or bathroom exhaust) in that space was activated and turned off when the corresponding PM2.5 sensor had three consecutive readings below 6 μg/m3. Advanced Control in the overall apartment reduced PM2.5 concentration by 40% compared to the Standard Control. The PM2.5 concentration difference between Advanced and Standard Control was ~ 20% in the kitchen. This can be attributed to using the stove hood manually in 66.5% of cooking PM2.5 emission events for 323.6 h compared to 88 h stove hood used in automated mode alongside 61.9 h and 33.7 h of PAC use in living room and bedroom, respectively. [ABSTRACT FROM AUTHOR]

Introduction: The effects of exposure to particulate matter and frailty, as well as its exposure-response relationship, have not been effectively explored. This study aimed to explore the association between long-term exposure to particulate matter and frailty state and each dimension in Chinese middle-aged and older adults, in addition to the exposure-response relationship. Methods: The data were obtained from the National Urban Air Quality Real-Time Dissemination Platform and China Health and Retirement Longitudinal Study (CHARLS). Frailty was measured by a frailty index containing 39 indicators. Annual averages of seven pollutants were calculated from hourly monitoring data. We used multilevel regression modeling to explore the association between long-term exposure to particulate matter and frailty. Meanwhile, we explored the exposure-response relationship based on a multilevel generalized summation model. We performed a sensitivity analysis using a multi-pollution model and a quantile-based g-computation (QGC) model. Results: A total of 15,611 participants were included in the analysis. We find that long-term exposure to PM2.5 was associated with an increased risk of pre-frailty and frailty (all p < 0.05). PMc and PM10 exhibited similar associations. The exposure-response relationship between PM2.5 showed a linear relationship, whereas the exposure-response relationship between PM10, PMc showed a nonlinear relationship. Elevated PM2.5 concentrations showed significant positive associations with the number of chronic disease score, IADL score, and functional limitation status score (all p < 0.05). PM10 and PMc showed similar positive correlations. These results remained robust after sensitivity analyses using a multi-pollution model and QGC model. Conclusion: Chronic exposure to particulate matter was significantly associated with increased risk of frailty. The exposure-response relationship between PM2.5 concentration and frailty showed a linear relationship, and the exposure-response relationship between PM10 and PMc showed a nonlinear relationship. Exposure to a mixture of pollutants carried a higher risk of frailty than exposure to a single pollutant. [ABSTRACT FROM AUTHOR]

Multivariate time series have more complex and high‐dimensional characteristics, which makes it difficult to analyze and predict the data accurately. In this paper, a new multivariate time series prediction method is proposed. This method is a generative adversarial networks (GAN) method based on Fourier transform and bi‐directional gated recurrent unit (Bi‐GRU). First, the Fourier transform is utilized to extend the data features, which helps the GAN to better learn the distributional features of the original data. Second, in order to guide the model to fully learn the distribution of the original time series data, Bi‐GRU is introduced as the generator of GAN. To solve the problems of mode collapse and gradient vanishing that exist in GAN, Wasserstein distance is used as the loss function of GAN. Finally, the proposed method is used for the prediction of air quality, stock price and RMB exchange rate. The experimental results show that the model can effectively predict the trend of the time series compared with the other nine baseline models. It significantly improves the accuracy and flexibility of multivariate time series forecasting and provides new ideas and methods for accurate time series forecasting in industrial, financial and environmental fields. [ABSTRACT FROM AUTHOR]

This study investigates the influence of digital finance tools on air quality in China from 1990 to 2020 using the cross-sectional augmented autoregressive distributed lag approach. The findings underscore the critical role of digital finance, especially internet banking, in reducing air pollution levels. The increased adoption of internet banking is linked to lower vehicular emissions and more efficient financial operations, aiding environmental preservation. Similarly, ATM banking is found to negatively correlate with air pollution, though to a lesser extent. In contrast, coal consumption, urban population growth, and inflation are positively associated with air pollution, highlighting the necessity for sustainable urban planning and energy transition policies. To combat air pollution, it is essential to utilize digital finance tools, broaden digitalization across various sectors, and employ AI and big data for precise interventions. Supporting green financial tools can help fund renewable energy projects, speeding up China's shift to a low-carbon economy and mitigating the harmful effects of air pollution. [ABSTRACT FROM AUTHOR]

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City's SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally. [ABSTRACT FROM AUTHOR]

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

A comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India. [ABSTRACT FROM AUTHOR]

Air quality in airport attracts a widespread attention due to the emission of GHGs and pollutants related with aircraft flight. Sustainable aviation fuel (SAF) has confirmed PM2.5 reduction due to free of aromatics and sulphur, and thus air quality improvement in airport is prospected by SAF blend. Two types of SAF were assessed the potential of energy saving and emission reduction by ZF850 jet engine. FT fuel is characterized with only paraffins without aromatics and cycloparaffins while HCHJ fuels is characterized with no aromatics. The descend of air quality and SAF blend were both investigated the effect on the engine performance and emission characteristic. The critical parameters were extracted from fuel compositions and air pollutants. Ambient air with a higher PM2.5 could lead to the rise of engine emission especially in UHC and PM2.5 despite at the low thrust setting and high thrust setting, and even couple with 3.2% rise in energy consumption and 1% reduction in combustion efficiency. CO, NO and NO2 in ambient air show less influence on engine performance and emission characteristic than PM2.5. Both types of SAF blend were observed significant reductions in PM2.5 and UHC. PM2.5 reduction obtained 37.9%—99.8% by FT blend and 0.64%-93.9% by HCHJ blend through the whole trust settings. There are almost 6.67% positive benefit in TSFC through the whole thrust setting by 7% FT blend. The effects of air quality and SAF blend on engine emission present significant changes on PM and UHC but the slight change on CO and NOx. By SAF blend, the energy saving and pollutant reduction obtained could be both benefit for air quality improvement in airport and further reduce engine emission as the feedback of less pollutants in ambient air. Highlights: Reduction of fuel consumption and engine emission by SAF blends; Higher PM in ambient air leads to the increase of fuel consumption and engine emission; Significantly reducing PM and UHC in airport by SAF blends; [ABSTRACT FROM AUTHOR]

Incubation of an empty nest is a rarely detected phenomenon in birds but has been documented in a diversity of species. The causes of this breeding anomaly are unclear, but previous accounts have occurred in regions with elevated air pollution. We provide a detailed account of a female Black-throated Blue Warbler (Setophaga caerulescens) incubating an empty nest for 8 d at the Hubbard Brook Experimental Forest, New Hampshire, USA, May–June 2023, and explore possible explanations based on a literature review of this breeding anomaly. Our observation coincided with the presence of smoke from widespread wildfires in Quebec, Canada. The fires were caused by record temperatures and dry conditions. Wildfire smoke increased air particle pollution. We found 231 published accounts of 11 species incubating empty nests. Cavity-nesters in Europe comprised 45% of species (5 of 11) and 96% of accounts (225 of 231). Although many accounts lacked a clear explanation, the most common factors hypothesized to explain incubating empty nests were exposure to environmental pollution (81% of accounts, 167 of 206), calcium deficiency from environmental acidification (19% of accounts, 40 of 206), and disease and/or infertility (4% of accounts, 8 of 206). Our synthesis of published observations highlights the need for more data on individuals expressing this unusual behavior. Such data would improve our understanding of how egg laying and reproductive success respond to intrinsic and environmental factors, including wildfire emissions, which are forecasted to increase with climate change. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are becoming a compelling acceleration strategy for geoscience numerical models due to their powerful computing performance. In this study, AMD's heterogeneous-compute interface for portability (HIP) was implemented to port the GPU acceleration version of the piecewise parabolic method (PPM) solver (GPU-HADVPPM) from NVIDIA GPUs to China's domestic GPU-like accelerators like GPU-HADVPPM4HIP. Further, it introduced the multi-level hybrid parallelism scheme to improve the total computational performance of the HIP version of the CAMx (Comprehensive Air Quality Model with Extensions; CAMx-HIP) model on China's domestic heterogeneous cluster. The experimental results show that the acceleration effect of GPU-HADVPPM on the different GPU accelerators is more apparent when the computing scale is more extensive, and the maximum speedup of GPU-HADVPPM on the domestic GPU-like accelerator is 28.9 × faster. The hybrid parallelism with a message passing interface (MPI) and HIP enables achieving up to a 17.2 × speedup when configuring 32 CPU cores and GPU-like accelerators on the domestic heterogeneous cluster. The OpenMP technology is introduced further to reduce the computation time of the CAMx-HIP model by 1.9 ×. More importantly, by comparing the simulation results of GPU-HADVPPM on NVIDIA GPUs and domestic GPU-like accelerators, it is found that the simulation results of GPU-HADVPPM on domestic GPU-like accelerators have less difference than the NVIDIA GPUs. Furthermore, we also show that the data transfer efficiency between CPU and GPU has a meaningful essential impact on heterogeneous computing and point out that optimizing the data transfer efficiency between CPU and GPU is one of the critical directions to improve the computing efficiency of geoscience numerical models in heterogeneous clusters in the future. [ABSTRACT FROM AUTHOR]

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies. [ABSTRACT FROM AUTHOR]

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68–0.9, Uexp = 16–66%, BSU = 0.1–0.7 µg/m3) and BC (R2 = 0.82–0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38–0.55, Uexp = 111–614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75–0.83, Uexp = 37–44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research. [ABSTRACT FROM AUTHOR]

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data. [ABSTRACT FROM AUTHOR]

The rapid transformation from conventional vehicles to electric vehicles (EVs) holds promise for enhancing air quality, reducing CO2 emissions, revitalizing the renewable energy market, and enhancing countries' economies. This study investigated the performance of EVs in Jordan and the key challenges hindering their rapid development. It implemented a structured questionnaire of 202 EV owners/drivers and 15 interviews with technicians at many service centers. Findings revealed that Jordan leads among MENA countries in Battery Electric Vehicles(BEVs) market share and growth rate, with 52% market share of BEVs new cars in 2023, projected to surpass 80% by 2025. This rapid development is tied up with many internal and external influences; government backing, high fossil fuel prices, and tax exemptions are common causes that enhanced the market of EVs. Respondents highlighted prevalent issues of BEVs, including the rapid battery depletion, low battery capacity, electric inverter malfunctions, battery overheating, low performance in hot and cold climates, and inflating costs. Finally, this study recommends educational institutions to offer targeted training programs for EV maintenance and service, and the government should provide more incentives for this industry, and combat the influx of counterfeit charging accessories, batteries, and spare parts. [ABSTRACT FROM AUTHOR]

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations. [ABSTRACT FROM AUTHOR]

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δ p - G f method based on the particle depolarization ratio at 355 nm ( δ p 355 ) and the fluorescence capacity ( G f ), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δ p 355 - G f method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δ p 355 - G f classification method and the δ p 355 - G f distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing. [ABSTRACT FROM AUTHOR]

Dental practices have evolved over time, introducing standardized safety protocols and enhancing safety standards. Regulatory bodies like OSHA, CDC, and ADA have set standards for infection control, chemical safety, and occupational health. Dental clinics face biological, chemical, and physical hazards. Maintaining high standards of EHS is crucial for patient and professional health, regulatory compliance, patient trust, and environmental sustainability. The COVID-19 pandemic has necessitated extra precautionary measures, and infection control measures have improved. Patients generally accept dental-dams and ergonomic interventions for infection control and safety. The aim of this study is to evaluate and enhance the environmental health and safety (EHS) protocols within dental clinics by examining key parameters that influence the well-being of both patients and dental professionals. Specifically, the study will focus on assessing air quality, infection control compliance, and the presence of isolated microorganisms in the workplace during invasive dental procedures. This study aims to assess environmental health and safety (EHS) parameters in dental clinics using a cross-sectional observational design. Participants include dental professionals and patients, who will evaluate infection control compliance and provide insights. Data will be collected from ten hospitals in Saudi Arabia, focusing on air quality, infection control compliance, and the presence of isolated microorganisms during invasive dental procedures. The study found that dental clinics have high concentrations of PM2.5 and CO2 in treatment rooms, indicating dental procedures. However, higher levels in waiting rooms suggest potential ventilation issues. Adherence to infection control protocols varied among dental professionals, with hand hygiene practices more prevalent among dental assistants. The study also highlighted the need for effective air filtration and control measures. The study on environmental health and safety in dental clinics reveals critical parameters affecting safety and quality. It highlights air quality, infection control compliance, and microbial contamination, emphasizing the need for high standards and effective cleaning and disinfection. [ABSTRACT FROM AUTHOR]

Global and South African populations continue to increase exponentially, with large flows of persons into urban centres. Urban air quality and bioaerosol are therefore becoming an increasingly important consideration in the face of a growing burden of allergic respiratory diseases. More than 20% of South Africans have asthma, with morbidity being disproportionately high compared to high-income countries (HICs). Air pollutants (particularly particulate matter and ozone), pollen and fungal spores are known triggers and exacerbating factors in asthma and, alarmingly, the levels of air pollutants remain high in hotspot areas despite robust air-quality legislation in South Africa. The concern is that genetically and environmentally vulnerable children in low- and middle-income countries (LMICs) such as South Africa will have asthma triggered and exacerbated disproportionately by poor air quality. Urban green areas are increasingly being recognised by many stakeholders as important possible mitigation tools. However, multiple factors must be considered for having an optimal green space design. In this review, we bring together experts across the fields of health, air quality and aerobiology to outline the intersection between asthma and air quality and/or aerobiology in South Africa. We highlight the importance of careful multidisciplinary green space design as a possible healing tonic. We conclude that detailed studies combining air quality, pollen dispersal and health issues are needed to derive more precise exposure--response functions of the green spaces in South Africa and other LMICs. [ABSTRACT FROM AUTHOR]

Background Recent evidence has linked air pollution with frailty, yet little is known about the role of NO2 in this association. Our aim was to assess the association between frailty and NO2 air concentrations in Mexican older adults. Methods We used georeferenced data from the population-based Nutrition and Health Survey in Mexico (NHNS) 2021, representative of national and subnational regions, to measure a frailty index based on 31 health deficits in adults aged 50 and older. Air pollution due to NO2 concentrations was estimated from satellite images validated with data from surface-level stations. Maps were produced using Jensen's Natural break method. The association of frailty and NO2 concentrations was measured using the frailty index (multivariate fractional response logit regression) and a frailty binary variable (frailty index [FI] ≥0.36, multivariate logit regression). Results There was a positive and significant association of the frailty index with the NO2 concentrations, adjusting for age, sex, urban and rural area, years of education, socioeconomic status, living arrangement, particulate matter smaller than 2.5 microns, and indoor pollution. For each standard deviation increase in NO2 concentrations measured 10 years before the survey, the odds of being frail were 15% higher, and the frailty index was 14.5% higher. The fraction of frailty attributable to NO2 exposure ranged from 1.8% to 23.5% according to different scenarios. Conclusions Frailty was positively associated with exposure to NO2 concentrations. Mapping frailty and its associated factors like NO2 air concentrations can contribute to the design of targeted pro-healthy aging policies. [ABSTRACT FROM AUTHOR]

A sustainable city is a smart city with a minimal impact on the environment, by incorporating technologies to reduce pollution. Traffic congestion which is a major concern contributes to global warming and climate change. Traffic forecasting projects future traffic patterns, using historical and current data to enhance traffic flow management. We propose a whole novel approach for predicting traffic congestion rate based on air quality data. We developed a new ensemble voting model based on Long Short Term Memory (LSTM) and Polynomial Regression (PolReg) models that use a new voting thresholded algorithm instead of the existing voting ones. The hyperparameters were optimized with the Genetic Agorithm, to overcome the non-stationarity of time series. A comparative study with the literature confirmed that our framework outperforms existing researches by keeping an absolute effectiveness according to learning curves, with Mean Absolute Error of 0.04, R-Squared of 0.93, and Root Mean Square Error (RMSE) of 0.05. [ABSTRACT FROM AUTHOR]

Comprehensive Air Quality Model with extensions (CAMx)–Decoupled Direct Method (DDM) simulations of first-order ozone (O 3) sensitivity to nitrogen oxides (NO x) and volatile organic compounds (VOCs) emissions were performed and combined with modelled P H 2 O 2 / P HN O 3 ratios to obtain a range of thresholds for determining O 3 -sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O 3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of P H 2 O 2 / P HNO 3 ratio. The O 3 concentration contributions in the newly added transition regime were apportioned to NO x and VOCs emissions in proportion to the relationship between the P H 2 O 2 / P HNO 3 ratio and first-order O 3 sensitivity. The source contributions of O 3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O 3 concentration contributions changed significantly in the NO x -limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O 3 to precursor emissions and may have far-reaching implications for informing O 3 pollution control policy. [ABSTRACT FROM AUTHOR]

Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%. [ABSTRACT FROM AUTHOR]

How atmospheric pollution is perceived by urban dwellers has long been a topic of interest within geography and the social sciences, whether to draw attention to environmental injustices, to better understand the materialities and affects associated with polluted air, or to grasp how people 'tune in' to polluted matter. In this paper, we draw on three interrelated geographical and social science literatures on polluted air to inform our exploration of how residents of an industrial town in the UK encounter and perceive localised ambient air pollution. Using creative methods, we explore residents' narrative accounts of everyday life in the town, revealing how their engagements with the matter of pollution over time are drawn from multiple registers, giving rise to a plurality of perceptions filled with tensions between near and far, and between past, present and future, producing an ambiguous atmosphere all of its own. The paper contributes to geographic explorations of urban atmospheres an understanding of how they are differently experienced and known, and how residential perceptions might persist or change over different timescales. [ABSTRACT FROM AUTHOR]

As Industry 4.0 develops, enterprises are investing in the industrial intelligent transformation. Using unique data from Chinese industrial enterprises, this paper investigates whether air quality affects the upgrading of industrial intelligence in enterprises. We find that decreased air quality improves industrial intelligence for enterprises, as enterprises increase their use of intelligent industrial robots when air pollution causes shortages of labour quantity and efficiency. Further analysis shows that large-scale enterprises and enterprises in areas with high economic development are more inclined to develop industrial intelligence in response to decreased air quality than their counterparts. In addition, the results indicate that the intelligent transformation of enterprises can improve their performance. This study provides a new perspective on the impact of environmental issues on corporate behaviour. [ABSTRACT FROM AUTHOR]

We study statistical inference procedures in coarsened time series through the generalized method of moments. A new model for the coarsened time series via multiple potential outcomes is proposed. It can be naturally extended for inferring multi‐variate coarsened time series. We show that this framework generates a general class of estimators. It neatly generalizes the classical Horvitz–Thompson estimator for handling coarsened time series data. Asymptotic properties, including consistency and limiting distribution, of the proposed estimators are investigated. Estimators of the optimal weight matrix and the long‐run covariance matrix are also derived. In particular, confidence intervals of the mean function of the potential outcome as a function of coarsening index can be constructed. A real‐data application on air quality in the USA is investigated. [ABSTRACT FROM AUTHOR]

Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process. [ABSTRACT FROM AUTHOR]