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The application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges. [ABSTRACT FROM AUTHOR]

Reactive dyes constitute a significant portion of colorants used in industries ranging from the textile industry to the paper industry. In most cases, the effluent streams from textile plants are highly colored, and treatment methods for dye decolorization such as chemical oxidation need to be explored. The oxidation processes investigated in this study are those of ozonation, ultrasound, and ultrasound-enhanced ozonation. The oxidation of an anthraquinone dye was studied under conditions of varying ultrasonic power, dye concentration, ozone concentration, pH, and temperature. Laboratory experiments were performed using a semibatch reactor by ozonating dye samples with and without ultrasound. Under conditions of constant ultrasonic radiation and continuous gas application, decolorization rates have been enhanced by ultrasound. The apparent first-order rate constants increased between 35 and 204% for the ultrasonic power inputs between 40 W/L and 120 W/L compared with ozonation alone. The effects of ultrasonic power input on the gas-liquid mass-transfer coefficient were also investigated and the results indicate that an increase in ultrasonic power input increases the mass-transfer coefficient. The mass-transfer coefficient increased between 89 and 93% for ozone inlet concentrations between 5.4 and 9.4 mg/L at an ultrasonic power of 120 W/L compared with ozonation alone. The reactions of the dye with ultrasound-enhanced ozone occurred through the hydroxyl radical pathway. [ABSTRACT FROM AUTHOR]

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

The paper provides a detailed analysis of excess mortality during the 'Spanish Flu' in a developing German economy and the effect of poverty and air pollution on pandemic mortality. The empirical analysis is based on a difference‐in‐differences approach using annual all‐cause mortality statistics at the parish level in the Kingdom of Württemberg. The paper complements the existing literature on urban pandemic severity with comprehensive evidence from mostly rural parishes. The results show that middle‐ and high‐income parishes had a significantly lower increase in mortality rates than low‐income parishes. Moreover, the mortality rate during the 1918 influenza pandemic was significantly higher in highly polluted parishes compared with least polluted parishes. Furthermore, the paper provides a detailed description of mortality statistics in Württemberg and new excess mortality rate estimates for Germany and its states. [ABSTRACT FROM AUTHOR]

The green hub‐and‐spoke airline network (GHSAN) is emerging as a dominant feature due to its excellent economic and environmental‐friendly capabilities. However, environmental GHSAN designs still have some concerns, including single pollutant‐domain oversimplification and lack of comprehensive network‐level operation impacts. This paper proposes a multi‐emission‐driven efficient network design approach for GHSAN, utilizing a system, green, and user threefold optimization methodology. The approach includes a multi‐objective optimization model and a two‐layer solving method. The multi‐objective optimization aims at minimizing multiple emissions, including carbon dioxide, carbonic oxide hydrocarbon, and nitric oxide, while also considering transportation system costs and journey user costs. A two‐layer optimization algorithm is adopted to address different scales of optimization. Real‐world results demonstrate that the proposed method mitigates environmental impact and user costs and increases overall airline density in airline networks. The proposed method can have a 16.29% reduction in green‐fold (10 nodes) and a 12.06% decrease in user costs for the user‐fold (10 nodes). As the number of nodes (15, 25, 50 nodes) and hubs (3, 4, 5, 6, 7 hubs) increase, the genetic algorithm (GA) proves to be more efficient and suitable in large‐scale GHSAN. This work is further significant for the long‐term and sustainable development of the future air transport industry. [ABSTRACT FROM AUTHOR]

Outdoor agricultural workers often work in harsh environmental conditions, including high temperatures and poor air quality. This paper studies how these factors impact worker productivity, which can have implications for worker health, well‐being, and income as well as farm payroll, production, and profitability. Our analysis uses 6 years of payroll records of harvesters on two large farms combined with pollution and weather monitor data from multiple sources. We address simultaneity issues by exploring pollution measurements from nearby upwind and downwind monitors and incorporating an alternative PM2.5 measure that better captures ambient or regional concentration. Across all specifications, results suggest that heightened concentrations of ground‐level ozone and PM2.5 are associated with reduced productivity. In our main specification, we find that one standard deviation increases in ozone and PM2.5 are associated with reductions in productivity of 2% and 1.1%, respectively. [ABSTRACT FROM AUTHOR]

The demand for vehicles in India is on a continuous rise, primarily attributed to increasing income levels. The Indian government has initiated steps to decarbonize the transport sector, offering incentives for alternate vehicles' adoption. This study introduces a qualitative framework, employing a causal structure to comprehend the dynamics of vehicle demand. The framework takes into account critical feedback loops that influence the demand for alternate vehicles and, consequently, the overall vehicle demand. The causal loop diagram for vehicle demand encompasses five key domains: socio‐economic development, infrastructure development, environmental awareness, fuel supply market and alternate vehicle technology. Notably, economic activity, urban sprawl, vehicle demand, alternate vehicle technology improvement, and the sustainability and fuel constraints linked with internal combustion engine vehicles emerge as the critical aspects governing the dynamics of the model. The leverage and intervention points influencing the vehicle demand are also identified. Further, based on the evolving interaction of the various reinforcing and balancing loops, this work identifies three distinct phases of the evolution of vehicle demand dynamics in India, namely, the emerging phase (2020–2040), the growth phase (2040–2060) and the stagnation phase (2060–2080). The paper discusses the crucial features of these phases. [ABSTRACT FROM AUTHOR]

Pollution usually accumulates on the surface of the insulator rapidly on high‐speed electric multiple units (EMUs) under operation condition, and the flashover can occur frequently, especially when the EMUs is running on a high‐altitude line. To solve the problem, this paper proposes the calculation method of insulator withstand voltage under the condition of non‐uniform fouling distribution of high‐speed airflow based on the characteristics of high altitude of Lanzhou‐Xin Line in China, and gives the design and test verification of insulator support insulator for 3500‐km altitude. The design and test verification of the supporting insulator of the rolling stock applicable to 3500 km altitude are presented. The result shows that the insulator should be reliable operation on 4430‐km high altitude area if the insulator structure height is 430 mm, the creep distance is 1250 mm. The anti‐pollution performance is fine in high‐speed wind if the radiuses of the large and small shed of the insulator are 196 and 145 mm, respectively, and the shed spacing is 45 mm. The 50‐Hz withstand voltage of the developed insulator is 131 kV and the light impulse voltage is 228 kV which is suitable for lines running on 3500 m. This research provides a theoretical basis and data to support the safety of high‐speed rolling stock in high‐altitude areas. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the association between ambient particulate matter no larger than 2.5 μm in diameter (PM2.5) during the prenatal and postnatal periods and infant neurodevelopmental parameters. Method: We conducted a population‐based birth cohort study using the Taiwan Birth Cohort Study. Participants were assessed for developmental conditions through home interviews at 6 months and 18 months of age. Exposure to PM2.5 of mothers and infants during perinatal periods was estimated using hybrid kriging/land‐use regression. The exposure was linked to each participant by home address. Logistic regression was then conducted to determine the risk of neurodevelopmental delay in relation to PM2.5. Results: A total of 17 683 term singletons without congenital malformations were included in the final analysis. PM2.5 during the second trimester was associated with increased risks of delays in gross motor neurodevelopmental milestones (adjusted odds ratio [aOR] 1.09 per 10 μg/m3 increase in exposure to PM2.5). Delayed fine motor development was also found to be related to exposure to PM2.5 in the second and third trimesters (aOR 1.06), as was personal–social skill (aOR 1.11 for the second trimester and 1.06 for the third). These neurodevelopmental parameters were unrelated to postnatal PM2.5 exposure. Interpretation: Exposure to ambient PM2.5 during pregnancy was significantly related to delay in gross motor, fine motor, and personal–social development in this population‐based study. What this paper adds: Prenatal exposure to higher PM2.5 was associated with increased risk of delayed early neurodevelopment.The critical period for delayed gross motor development was the second trimester.The critical period for fine motor and personal–social development was the second and third trimesters. [ABSTRACT FROM AUTHOR]

Pregnancy and early childhood are periods with high plasticity in neurological development. Environmental perturbations during these sensitive windows can have lifelong developmental consequences. This review summarizes key findings relevant to the effects of air pollution on neurological development. Mounting evidence suggests that exposure to air pollution, both during pregnancy and childhood, is associated with childhood developmental outcomes ranging from changes in brain structures to subclinical deficits in developmental test scores, and, ultimately, developmental disorders such as attention‐deficit/hyperactivity disorders or autism spectrum disorders. Although the biological mechanisms of effects remain to be elucidated, multiple pathways are probably involved and include oxidative stress, inflammation, and/or endocrine disruption. Given the alarming global increase in developmental disorders in recent years, and increased human exposures to pollution, it is critical to reduce personal and community‐level exposures through tight collaboration of interdisciplinary and multi‐level bodies including community partners, physicians, industry partners, policy makers, public health practitioners, and researchers. What this paper adds: Exposure to air pollution is associated with a range of childhood developmental complications.Biological mechanisms may include oxidative stress, inflammation, and endocrine disruption. What this paper adds: Exposure to air pollution is associated with a range of childhood developmental complications.Biological mechanisms may include oxidative stress, inflammation, and endocrine disruption. [ABSTRACT FROM AUTHOR]

Due to deforestation, greenhouse gases and increasing air conditioner systems, the temperatures are changing drastically and the effects are prominent in summer and winter seasons. These ambient temperature variations are affecting the transmission system parameters and also the voltages at various buses. Changes in the transmission line parameters have non-negligible impact on system performance and present Energy Management applications are not considering these temperature variations in the power system state estimation. In this paper, impact of temperature variations on the power system Voltage Stability assessment is considered. Voltage stability assessment methods make use of the load flow results. Load flow results without considering the temperature variations are inaccurate in the real-time and leading to the erroneous results in the voltage stability assessment. In view of this, studies have been carried out on IEEE 30-bus, 118-bus and 300-bus systems, incorporating the seasonal temperature variations on various types of conductors, and also demonstrated the impact with conventional and ZIP type load models. In this paper, four voltage stability indices NLVSI, $L_{mn}$Lmn , FVSI and VCPI(power) are used for identifying the proximity of voltage collapse point, critical line, critical bus voltage, critical bus angle, total system active power loss, total system reactive power loss etc. [ABSTRACT FROM AUTHOR]

Low‐cost air quality monitoring networks can potentially increase the availability of high‐resolution monitoring to inform analytic and evidence‐informed approaches to better manage air quality. This is particularly relevant in low and middle‐income settings where access to traditional reference‐grade monitoring networks remains a challenge. However, low‐cost air quality sensors are impacted by ambient conditions which could lead to over‐ or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large‐scale calibration of AirQo sensors, low‐cost PM sensors custom‐designed for and deployed in Sub‐Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM using k‐nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5 and PM10 calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m3 to 7.2 μg/m3 with an average BAM PM2.5 concentration of 37.8 μg/m3 while the lasso regression model decreased RMSE from 13.4 μg/m3 to 7.9 μg/m3 with an average BAM PM10 concentration of 51.1 μg/m3. We validate our models through cross‐unit and cross‐site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large‐scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting. [ABSTRACT FROM AUTHOR]

This paper investigates whether air quality affects corporate investment. Using a sample of Chinese A‐share listed firms from 2007 to 2013, this paper finds that poor air quality is negatively associated with corporate investment and reduction of corporate investment due to air pollution intensifies with manager risk aversion. In addition, this paper provides evidence against the alternative explanation that corporate investment decreases due to government air‐quality regulations. Furthermore, this paper also suggests that poor air quality is negatively associated with investment efficiency. These findings complement existing literature on how weather conditions affect corporate financial decisions. [ABSTRACT FROM AUTHOR]

While multiple information sources exist concerning surface‐level air pollution, no individual source simultaneously provides large‐scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross‐validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground‐based measurements only. Results also indicate the potential for near‐term updating of forecasts based on recent ground measurements. Plain Language Summary: Air quality is a major health concern worldwide, leading to millions of premature deaths annually. In order to better understand this risk and mitigate its impacts, there are numerous sources of information about air quality. These include ground‐based measurement stations, satellites, and global air quality models. By combining these data sources together, we can use the strengths of each source to compensate for the weaknesses of others. This paper presents one method of combining these data sources and uses it to make air quality forecasts over five US cities up to one day in advance. These forecasts are compared to pollution estimates made using ground‐based measurement data only to see how integrating additional data sources improves the forecast. Overall, we find that there are large increases in accuracy of forecasting using the proposed method, and that further improvements can be made by comparing the forecasts to the most recent ground‐based measurements and making some more final adjustments. Methods like this, which use a combination of globally available satellite and model data together with some local measurements, can be applied to different types of air pollution in all regions of the world, thereby improving our understanding of air pollution globally. Key Points: Multiple air quality data sources (GOES‐CF model, TROPOMI satellite, Environmental Protection Agency monitors) are combined to improve city‐scale NO2 forecastsForecasts using combined data outperform forecasts using ground‐based measurements onlyUpdating of forecasts based on residuals against the most recent ground measurements further improves short‐term forecasting [ABSTRACT FROM AUTHOR]

Spatial hot spots of COVID‐19 infections and fatalities are observed at places exposed to high levels of air pollution across many countries. This study empirically investigates the relationship between exposure to air pollutants that is, sulfur dioxide, nitrogen dioxide, and particulate matter (SO2, NO2, and PM10) and COVID‐19 infection at the smallest administrative level (ward) of Mumbai City in India. The paper explores two hypotheses: COVID‐19 infection is associated with air pollution; the pollutants act as determinants of COVID‐19 deaths. Kriging is used to assess the spatial variations of air quality using pollution data, while information on COVID‐19 are retrieved from the database of Mumbai municipality. Annual average of PM10 in Mumbai over the past 3 years is much higher than the WHO specified standard across all wards; further, suburbs are more exposed to SO2, and NO2 pollution. Bivariate local indicator of spatial autocorrelation finds significant positive relation between pollution and COVID‐19 infected cases in certain suburban wards. Spatial Auto Regressive models suggest that COVID‐19 death in Mumbai is distinctly associated with higher exposure to NO2, population density and number of waste water drains. If specific pollutants along with other factors play considerable role in COVID‐19 infection, it has strong implications for any mitigation strategy development with an objective to curtail the spreading of the respiratory disease. These findings, first of its kind in India, could prove to be significant pointers toward disease alleviation and better urban living. Plain Language Summary: The study investigates the relationship between exposure to pollutants (local SO2, NO2, and PM10) and COVID‐19 (cases and deaths) in Mumbai. It also explains whether pollutants act as determinants of COVID‐19 deaths when other factors like population density, health infrastructure, number of slums, waste water drains and roads are controlled through spatial modelling. PM10 concentration is much above the WHO specified standard across Mumbai. Overall, people living in the suburbs of Mumbai are at a higher risk of respiratory morbidity. High exposure to specific pollutants (SO2, NO2, and PM10) and high‐presence of COVID‐19 cases are well established in certain wards in the suburban parts of the city. Further, exposure to NO2 and COVID‐19 deaths reveals strong linkages in Mumbai. Association of COVID‐19 and environmental pollution should be considered as a measure of an integrated approach in sustainable development as it has strong implications for mitigation strategies related to the novel virus. Key Points: Significant association between COVID‐19 infection with SO2, NO2, and PM10 reveals spatial hot spots in MumbaiWith increase in NO2 there is substantial increase in COVID‐19 deaths in MumbaiAir quality is an important element to address COVID‐19 management and sustainable urban development [ABSTRACT FROM AUTHOR]

Externalities, such as air pollution and increased occupational hazards, resulting from global trends in climate change, rapid industrialization, and rapidly increasing populations are raising global concerns about the associated health risks. The Global Environmental and Occupational Health Hub for Eastern Africa was established to address some of these problems at national and regional levels through focused training and applied research that would yield evidence supporting policies and investments to mitigate risks of increasing environmental threats throughout the Eastern African region. Emphasis has been placed on air pollution, a leading risk factor for global mortality, accounting for over 7 million premature deaths or 8.7% of the 2017 global mortality burden. Despite the enormous disease burden that air pollution causes, global investment in air pollution monitoring and research capacity building in low‐middle and middle‐income countries have been inadequate. This study outlines the activities the Hub has undertaken in planning for and carrying out its initial capacity building and building its primary research programs and identifies central lessons that can inform other large global research partnerships. Plain Language Summary: Air pollution and increased occupational hazards resulting from climate change and rapid industrialization are damaging health. The Global Environmental and Occupational Health (GEOHealth) Hub for Eastern Africa was established to address some of these problems at national and regional levels through training and applied research. This paper describes the activities the Hub has undertaken during its first five years and identifies central lessons that can inform other large global research partnerships. Key Points: Environmental and occupational health challenges remain significant throughout Eastern AfricaThe Eastern Africa GEOHealth Hub is well positioned to build its research and dissemination activitiesThe Hub serves as a model for other large, international research collaborations [ABSTRACT FROM AUTHOR]

The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross‐validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by −53 ± 2%, −45 ± 11%, −30 ± 13%, and −15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic‐busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence. Key Points: A data driven modeling is applied to quantify the reduction of air pollution during the Corona Virus disease 2019 outbreak in Quito, EcuadorThe accuracy of the models is high (mean PCC = 0.78), especially for predicting NO2 (mean PCC = 0.87) and CO (mean PCC = 0.86)The average drop of pollution during the lockdown is: −53% for NO2, −45% for SO2, −30% for CO, and −15% for PM2.5The industrial areas are less impacted by the quarantine than the traffic and residential districtsThe concentration of pollution tends to return to usual levels, as soon as the relaxed restriction is implemented [ABSTRACT FROM AUTHOR]

This paper aims to correct standard measures of agglomeration economies to account for air pollution. This paper examines the impact of nitrogen oxide (NOX) a pollutant mainly released by transportation on worker productivity. Literature on agglomeration economies highlights the positive role of employment density on productivity, without accounting for the negative impacts of local pollutants. First, standard estimates of agglomeration economies for the 304 French employment areas are in line with the literature. Then, we introduce the variable (NOX) emissions and, once pollution is accounted for, the estimated direct impact of agglomeration externalities on productivity are lower by more than 13 per cent. [ABSTRACT FROM AUTHOR]

The topic of increasing the reflectivity of the Earth as a measure to counteract global warming has been the subject of high-level discussions and preliminary research since several decades, though prior to the early 2000s there was only very limited research on the topic. This changed in the mid-2000s, particularly following the publication of a special section of Climatic Change with a lead paper by Crutzen [2006], which posited the idea of stratospheric aerosol injections as a possible solution to a policy dilemma. The discussions around the publication of Crutzen [2006] demonstrated how contentious the topic was at that time. The special section of Climatic Change contributed to breaking the 'taboo' on albedo modification research that was perceived at that time, and scientific publications on the topic have since proliferated, including the development of several large national and international projects, and the publication of several assessment reports over the last decade. Here we reflect on the background and main conclusions of the publications in 2006, the developments since then, and on some of the main developments over the next decade that we anticipate for research and dialogue in support of decision-making and policy development processes. [ABSTRACT FROM AUTHOR]

Nowadays, running out of fossil fuels and more attention to reduce environmental pollutions are essential factors for the growing use of electric vehicles (EVs). Owing to these factors, it is important to present a method, which schedules charging or discharging of EVs and simultaneously considers economic and environmental aspects of the problem. This study proposes a multi‐objective optimisation programme for charging or discharging of EVs in a smart distribution system by taking advantage of advanced metering infrastructure and uses a ɛ‐constraint method for minimising operational costs and (CO2) emissions. Simulating stochastic patterns of EV owner driving behaviour as well as considering different models and types of EVs with the help of trip planning algorithm are the main advantages of this study. Investigating the multi‐objective problem in two cases shows the effectiveness and flexibility of this algorithm in real cases. Besides, vehicle‐to‐grid capability of EVs was also considered. This method was tested on a 33‐bus distribution test system for over 24 h. As the results show, the total amount of scheduled power, the peak‐to‐valley difference of daily load, transmission power loss, CO2 emission, and the total operational cost are reduced by the trip planning programme and EV owner revenue is increased. [ABSTRACT FROM AUTHOR]

Air pollution in India is a foremost environmental risk factor that affects human health. This study first investigates the geographical distribution of ambient and household air pollution (HAP) and then examines the associated mortality risk. Data on fine particulate matter (PM2.5) concentration has been extracted from the Greenhouse Gas Air Pollution Interactions and Synergies (GAINS) model. HAP, mortality and socio‐demographic data were extracted from the National Family and Health Survey‐5, India, 2019–2021. Regression models were applied to see the difference in age‐group mortality by different pollution parameters. The districts with PM2.5 concentration above the National Ambient Air Quality Standard (NAAQS) level of 40 μg/m3 show a higher risk of neonatal (OR‐1.86, CI 1.418–2.433), postneonatal (OR‐2.04, CI 1.399–2.971), child (OR‐2.19, CI 0.999–4.803) and adult death (OR‐1.13, CI 1.060–1.208). The absence of a separate kitchen shows a higher probability of neonatal (OR: 1.18, CI 1.074–1.306) and adult death (OR‐1.06, CI 1.027–1.088). The interaction between PM2.5 levels above NAAQS and HAP leads to a substantial rise in mortality observed for neonatal (OR 1.19 CI 1.051–1.337), child (OR 1.17 CI 1.054–1.289), and adult (OR 1.13 CI 1.096–1.168) age groups. This study advocates that there is a strong positive association between ambient and HAP and mortality risk. PM2.5 pollution significantly contributes to the mortality risk in all age groups. Children are more vulnerable to HAP than adults. In India, policymakers should focus on reducing the anthropogenic PM2.5 emission at least to reach the NAAQS, which can substantially reduce disease burden and, more precisely, mortality. Plain Language Summary: Air pollution in India poses a significant threat to human health, as explored in this study. Using data from the Greenhouse Gas Air Pollution Interactions and Synergies model and the National Family and Health Survey‐5, the research examines both ambient and household air pollution and their impact on mortality risk. Analyzing fine particulate matter (PM2.5) concentration, the study finds that districts exceeding the National Ambient Air Quality Standard (NAAQS) of 40 μg/m3 face increased mortality risks across all age groups, including neonatal, postneonatal, child, and adult deaths. Additionally, the absence of separate kitchens correlates with higher neonatal and adult mortality rates. The interaction between elevated PM2.5 levels and household air pollution further amplifies mortality risks, particularly among neonates, children, and adults. The study underscores the urgent need for policymakers to mitigate anthropogenic PM2.5 emissions to meet NAAQS standards, which could significantly reduce mortality and disease burden, particularly among vulnerable populations like children. Key Points: Interaction between PM2.5 levels above National Ambient Air Quality Standard and household air pollution, leads to a substantial rise in mortality observed for all age‐groupsDistricts with PM2.5 levels exceeding the National Ambient Air Quality Standard show an approximately two fold increase in the odds of mortality among children [ABSTRACT FROM AUTHOR]

Wildfires have long been regarded as one chief culprit in regional air pollution, and pose great impacts on climate change. Although climate forcing of wildfire smoke has been widely investigated, its influence on synoptic systems remains unclear. Based on measurement and modeling analysis, the impact of wildfire smoke on the development of a mid‐latitude cyclone was revealed for Canadian wildfires in early June of 2023. The radiative forcing induced by smoke at surface and in the atmosphere reached up to −150 and 100 W m−2, posing opposite tendencies of atmospheric stratification over the land and ocean. Such perturbations contributed to the enhancement and stagnation of the cyclone, which favored the transport of smoke from the fire‐intensive region, indicated by nearly 40% increment of PM2.5 mass flux. With escalating wildfire risk in the future, the inclusion of smoke aerosols' impacts on meteorology in weather forecast models is of great importance. Plain Language Summary: Wildfires are uncontrolled fires that burn in the wildland vegetation, posing great challenges to regional air quality and global climate. Wildfire smoke has been known to exert great climate forcing via aerosol‐radiation interaction yet its impact on synoptic scales needs further investigation. Here, based on comprehensive observations and modeling analysis for the extreme Canadian wildfires in early June of 2023, smoke aerosol is revealed to induce significant radiative forcing and yield opposite modifications of temperature stratification over the land and ocean, resulting in intensified and stagnant mid‐latitude cyclone. Such perturbations favored the transport of smoke from fire‐intensive region to downwind cities in Canada and United States, and the subsequent long‐range transport dominated by the cyclone. Key Points: Smoke from intensive Canadian wildfires in early June of 2023 degraded the air quality of cities in eastern Canada and United StatesThe wildfire smoke enhanced the development of the mid‐latitude cyclone via smoke aerosol‐radiation interactionThe intensification and stagnation of the cyclone facilitated the transport of smoke to downwind cities in northeastern United States [ABSTRACT FROM AUTHOR]

Objective: Air pollutants have been reported to have a potential relationship with amyotrophic lateral sclerosis (ALS). The causality and underlying mechanism remained unknown despite several existing observational studies. We aimed to investigate the potential causality between air pollutants (PM2.5, NOX, and NO2) and the risk of ALS and elucidate the underlying mechanisms associated with this relationship. Methods: The data utilized in our study were obtained from publicly available genome‐wide association study data sets, in which single nucleotide polymorphisms (SNPs) were employed as the instrumental variantswith three principles. Two‐sample Mendelian randomization and transcriptome‐wide association (TWAS) analyses were conducted to evaluate the effects of air pollutants on ALS and identify genes associated with both pollutants and ALS, followed by regulatory network prediction. Results: We observed that exposure to a high level of PM2.5 (OR: 2.40 [95% CI: 1.26–4.57], p = 7.46E‐3) and NOx (OR: 2.35 [95% CI: 1.32–4.17], p = 3.65E‐3) genetically increased the incidence of ALS in MR analysis, while the effects of NO2 showed a similar trend but without sufficient significance. In the TWAS analysis, TMEM175 and USP35 turned out to be the genes shared between PM2.5 and ALS in the same direction. Conclusion: Higher exposure to PM2.5 and NOX might causally increase the risk of ALS. Avoiding exposure to air pollutants and air cleaning might be necessary for ALS prevention. [ABSTRACT FROM AUTHOR]

1. The number of published meta-analyses in plant ecology has increased greatly over the last two decades. Meta-analysis has made a significant contribution to the field, allowing review of evidence for various ecological hypotheses and theories, estimation of effects of major environmental drivers (climate change, habitat fragmentation, invasive species, air pollution), assessment of management and conservation strategies, and comparison of effects across different temporal and spatial scales, taxa and ecosystems, as well as research gap identification. 2. We identified 322 meta-analyses published in the field of plant ecology between 1996 and 2013 in 95 different journals and assessed their methodological and reporting quality according to standard criteria. Despite significant recent developments in the methodology of meta-analysis, the quality of published meta-analyses was uneven and showed little improvement over time. 3. We found many cases of imprecise and inaccurate usage of the term 'meta-analysis' in plant ecology, particularly confusion between meta-analysis and vote counting and incorrect application of statistical techniques designed for primary studies to meta-analytical data, without recognition of the violation of statistical assumptions of the analyses. 4. Methodological issues for meta-analyses in plant ecology include incomplete reporting of search strategy used to retrieve primary studies, failure to test for possible publication bias and to conduct sensitivity analysis to test the robustness of the results, as well as lack of availability of the data set used for the analyses. 5. The use of meta-analysis is particularly common in community ecology, ecophysiology and ecosystem ecology, but meta-analyses in ecophysiology are more likely not to meet standard quality criteria than papers in other subdisciplines. Fewer meta-analyses have been conducted in plant population ecology. 6. Synthesis. Over the past two decades, plant ecologists have embraced meta-analysis as a statistical tool to combine results across studies, and much has been learned as a result. However, as the popularity and usage of meta-analysis in the field of plant ecology has grown, establishment of quality standards, as has been done in other disciplines, becomes increasingly important. In order to improve the quality of future meta-analyses in plant ecology, we suggest adoption of a checklist of quality criteria for meta-analysis for use by research synthesists, peer reviewers and journal editors. [ABSTRACT FROM AUTHOR]

mHealth, such as apps running on consumer smart devices is becoming increasingly popular and has the potential to profoundly affect healthcare and health outcomes. However, it may be disruptive and results achieved are not always reaching the goals. Allergic Rhinitis and its Impact on Asthma (ARIA) has evolved from a guideline using the best evidence‐based approach to care pathways suited to real‐life using mobile technology in allergic rhinitis (AR) and asthma multimorbidity. Patients largely use over‐the‐counter medications dispensed in pharmacies. Shared decision making centered around the patient and based on self‐management should be the norm. Mobile Airways Sentinel networK (MASK), the Phase 3 ARIA initiative, is based on the freely available MASK app (the Allergy Diary, Android and iOS platforms). MASK is available in 16 languages and deployed in 23 countries. The present paper provides an overview of the methods used in MASK and the key results obtained to date. These include a novel phenotypic characterization of the patients, confirmation of the impact of allergic rhinitis on work productivity and treatment patterns in real life. Most patients appear to self‐medicate, are often non‐adherent and do not follow guidelines. Moreover, the Allergy Diary is able to distinguish between AR medications. The potential usefulness of MASK will be further explored by POLLAR (Impact of Air Pollution on Asthma and Rhinitis), a new Horizon 2020 project using the Allergy Diary. [ABSTRACT FROM AUTHOR]

Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely‐existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non‐polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ‐5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients' literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32. [ABSTRACT FROM AUTHOR]

Various fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate. Plain Language Summary: We studied how much carbon monoxide (CO) was released into the air in southern Africa due to fires. To derive CO emissions we used different remotely sensed data of area burned, with varying levels of detail, from 500 to 20 m, and used these emissions in a model to mimic atmospheric transport and chemistry of CO. We found that emissions derived from the very detailed 20‐m satellite images from Sentinel‐2 produced a similar large amount of CO in the atmosphere over southern Africa as seen by CO satellite observations. This is because the smaller fires, which are harder to spot, were detected with the 20‐m burned area satellite observations. We also found that the air movement and inflow of polluted air from outside Africa impacted the peak of air pollution. Key Points: Sentinel‐2 burned area detects small fires, resulting in up to 120% higher fire emissions compared to conventional resolution estimatesImproved fuel load modeling and new emission factors yield more trustworthy fire emissions that can be evaluated with top‐down constraintsAtmospheric modeling and TROPOMI CO observations indicate a 15% bias reduction using Sentinel‐2 burned area compared with coarser fire data [ABSTRACT FROM AUTHOR]

Background: The skin is the largest organ in the human body, not only resisting the invasion of harmful substances, but also preventing the loss of moisture and nutrients. Maintaining skin homeostasis is a prerequisite for the proper functioning of the body. Any damage to the skin can lead to a decrease in local homeostasis, such as ultraviolet radiation, seasonal changes, and air pollution, which can damage the skin tissue and affect the function of the skin barrier. Objective: This article reviews the maintenance mechanism and influencing factors of skin homeostasis and the symptoms of homeostasis imbalance. Methods: We searched for articles published between 1990 and 2022 in English and Chinese using PubMed, Web of Science, CNKI, and other databases in the subject area of dermatology, using the following search terms in various combinations: "skin homeostasis," "skin barrier," and "unstable skin." Based on our results, we further refined our search criteria to include a series of common skin problems caused by the destruction of skin homeostasis and its treatments. Limitations include the lack of research on dermatological and cosmetic problems triggered by the disruption of skin homeostasis. Results: This study describes the neuroendocrine‐immune system, skin barrier structure, and skin metabolic system that maintain skin homeostasis. In addition, we discuss several common symptoms that occur when skin homeostasis is out of balance, such as dryness, redness, acne, sensitivity, and aging, and explain the mechanism of these symptoms. Conclusion: This article provides an update and review for students and practitioners, and provides a theoretical basis for the development of skin care products for the maintenance and repair of skin homeostasis. [ABSTRACT FROM AUTHOR]

In this review, we discuss some of the recent advances in our understanding of the physiology of the air pollution and exercise. The key areas covered include the effect of exercise intensity, the effects of pre‐exposure to air pollution, acclimation to air pollution, and the utility of masks during exercise. Although higher intensity exercise leads to an increase in the inhaled dose of pollutants for a given distance traveled, the acute effects of (diesel exhaust) air pollution do not appear to be more pronounced. Second, exposure to air pollution outside of exercise bouts seems to have an effect on exercise response, although little research has examined this relationship. Third, humans appear to have an ability to acclimate to ground level ozone, but not other pollutants. And finally, masks may have beneficial effects on certain outcomes at low intensity exercise in pollution with significant levels of particles, but more study is required in realistic conditions. [ABSTRACT FROM AUTHOR]

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The terms “grandfather clause” and “grandfathering” describe elements of a policy programme in which existing participants in an activity are protected from the impact of regulations, restrictions or charges applied to new entrants. In this paper, the role of grandfathering in the design of a carbon emissions trading scheme in Australia is assessed. It is argued that adjustment assistance policies such as those adopted in conjunction with previous microeconomic reform programmes are preferable to policies based on the free issue of emission permits. The suggestion that owners of capital assets should be compensated for changes in government policy that reduce the expected flow of income from those assets represents a radical, and undesirable, policy innovation. [ABSTRACT FROM AUTHOR]

The transport sector is consistently responsible for around 30 per cent of carbon dioxide emissions in developed countries and is one of few sectors where emissions continue to increase as a result of apparently insatiable demand for road and air travel. This paper examines how the formulation of transport policy fits into the exposition of UK climate policy, focusing on three principal areas of tension: policy priority (congestion and carbon reduction); strategies to reduce emissions (technological and behavioural solutions) and timescale (short- and long-term vision). We suggest that in overcoming such tensions government ministers will need to devolve significant policy formulation and implementation powers to an appropriate scale of governance – in this case the city-region – to fashion a ‘convergence space’ capable of promoting meaningful action with regard to transport's climate impact. [ABSTRACT FROM AUTHOR]

Background: Despite public awareness that tobacco secondhand smoke (SHS) is harmful, many people still assume that marijuana SHS is benign. Debates about whether smoke-free laws should include marijuana are becoming increasingly widespread as marijuana is legalized and the cannabis industry grows. Lack of evidence for marijuana SHS causing acute cardiovascular harm is frequently mistaken for evidence that it is harmless, despite chemical and physical similarity between marijuana and tobacco smoke. We investigated whether brief exposure to marijuana SHS causes acute vascular endothelial dysfunction., Methods and Results: We measured endothelial function as femoral artery flow-mediated dilation (FMD) in rats before and after exposure to marijuana SHS at levels similar to real-world tobacco SHS conditions. One minute of exposure to marijuana SHS impaired FMD to a comparable extent as impairment from equal concentrations of tobacco SHS, but recovery was considerably slower for marijuana. Exposure to marijuana SHS directly caused cannabinoid-independent vasodilation that subsided within 25 minutes, whereas FMD remained impaired for at least 90 minutes. Impairment occurred even when marijuana lacked cannabinoids and rolling paper was omitted. Endothelium-independent vasodilation by nitroglycerin administration was not impaired. FMD was not impaired by exposure to chamber air., Conclusions: One minute of exposure to marijuana SHS substantially impairs endothelial function in rats for at least 90 minutes, considerably longer than comparable impairment by tobacco SHS. Impairment of FMD does not require cannabinoids, nicotine, or rolling paper smoke. Our findings in rats suggest that SHS can exert similar adverse cardiovascular effects regardless of whether it is from tobacco or marijuana., (© 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.)

This paper outlines a general approach for analyzing the role of culture in international environmental policymaking. It draws on work in anthropology and foreign policy analysis. The first step is to view culture as a ‘toolkit of environmental ideas.’ The second step, relative to a given research topic, is to delimit broad definitions of culture to more workable forms. Three forms are offered (following Hudson, 1997a): culture as organization of environmental meaning, as shared value preferences in environmental matters, and as templates for environmental action. The third step is to answer three basic questions relative to the specific definition of culture used: (a) Who draws what environmentally related ideas from the ideas toolkit? (b) How are these ideas used in the political arena? (c) How do these ideas, originally drawn upon for political purposes, change and in turn lead to changes in the set of environmentally related ideas in the culture? Ideas, once they have entered the political arena, are assumed to be embodied in a ‘discourse.’ The terminology of discourse (and the body of theory built up around it) is used as a vehicle for examining the role of culture in international environmental policymaking. A practical application of this approach is presented in relation to the role of culture in the Japanese public's influence in Japan on policymaking on the northeast Asian transboundary air pollution issue. [ABSTRACT FROM AUTHOR]

Over the past decade, straw burning has been debated as one of the causes of severe air pollution in developing countries. However, the magnitude and pattern of its impact on air quality have not been precisely estimated. This study employs remote sensing data on straw fires from different satellite sources and examines its overall impact on urban air quality in China. Exploiting daily variations in straw burning activities and air pollution with a difference‐in‐differences strategy across 290 cities and 620 days, I find a clear temporal pattern for the straw burning effect. On the first day after burning, the index of air pollution increases by 6.5 in urban areas, equivalent to 9.4% of the mean. This impact decreases over time and remains significant for at least eight days. The effect is larger for fires located in the upwind direction relative to urban areas and is limited with lower wind speed. In terms of distance, pollution is mostly driven by straw fires within 100 km from urban centers but could also be influenced by fires as far as 600 km away. Among different pollutants, particulate matters are increased most by straw fires. The effect is largest and most persistent in October and November, when straw burning prevails in the north after harvest, but is also non‐negligible in other seasons. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable increase in the consumption of fossil energy, leading to a significant rise in environmental pollution, particularly in China due to its rapid development. This has resulted in the frequent occurrence of large‐scale fog and haze weather, highlighting the urgent need for environmental protection measures. To gain insights into the atmospheric conditions in China, an analysis was conducted on the wet deposition of polycyclic aromatic hydrocarbons (PAHs) in a remote region of Central South China from 2014 to 2017. The study revealed that the average concentrations and peak values of Ʃ16PAHs in 2014 and 2015 were considerably higher than those observed in 2016 and 2017. Furthermore, it was found that five‐ring PAH species were the predominant components during 2014 and 2015, indicating a shift in the main sources of PAHs. The peaks of Ʃ16PAHs were predominantly detected in samples collected during light rain in the winter, specifically on days without heavy rainfall. This can be attributed to the absence of heavy rain, which would otherwise reduce the concentration of air pollutants. Consequently, contaminants accumulated in the air are easily enriched in rainwater. The concentrations of Ʃ15Alkyl‐PAHs also exhibited a significant correlation with the number of rainfall days. Notably, a much higher annual average concentration of Ʃ15Alkyl‐PAHs was observed in 2017, which experienced fewer rainfall days. Coal combustion, petroleum sources, and vehicular emissions accounted for 58%, 12%, and 30% of the PAHs in the air, respectively. Despite improvements in air quality in China since 2016, it is crucial to address the elevated concentrations of PAHs in the atmosphere, particularly under adverse meteorological conditions characterized by reduced rainfall. [ABSTRACT FROM AUTHOR]

Among various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data. [ABSTRACT FROM AUTHOR]

The study proposes a dynamic spatio‐temporal profile of the distribution of tuberculosis incidence and air pollution in Romania, where this infectious disease induces more than 8,000 new cases annually. The descriptive analysis for the years 2012–2021 assumes an identification of the structuring patterns of mycobacterium tuberculosis risk in the Romanian population, according to gender and age, exploiting spatial modeling techniques of time series data. Through spatial autocorrelation, the degree of similarity between the analyzed territorial systems was highlighted and the relationships that are built between the analysis units in spatial proximity were investigated. By modeling the geographical distribution of tuberculosis, the spatial correlation with particulate matter (PM2.5) pollution was revealed. The identification of clusters of infected persons is an indispensable step in the construction of efficient tuberculosis management systems. The results highlight the link between the distribution of tuberculosis, air pollution and socio‐economic development, which requires a detailed analysis of the epidemiological data obtained in the national tuberculosis surveillance and control program from the perspective of geographical distribution. Plain Language Summary: The study aims to identify the main geographical areas of tuberculosis concentration in Romania, which are among the highest in Europe. At the EU level, public health policies aim to strengthen integrated care services and further implement patient‐centered approaches. Annual data at the level of territorial administrative unit, by age and gender from the national demographic statistics, published on the official website of the National Institute of Statistics, were used to relate the number of cases to the population. The maps were produced using geospatial data published by the National Agency for Cadastre and Real Estate Publicity and the official nomenclature of administrative territorial units of Romania, called SIRUTA. Statistical data on the distribution of tuberculosis cases in Romania were obtained from the annual reports of the National Tuberculosis Prevention, Surveillance and Control Program in Romania. The research aimed to compare the geographical distribution of tuberculosis over the distribution of areas with the highest values of particulate matter (PM2.5). Accurate identification of tuberculosis concentration areas and correlation with PM2.5 provides relevant data to optimize disease management policies. Key Points: The study creates a dynamic spatio‐temporal profile of the tuberculosis frequency distribution (incidence) in RomaniaThe study analyses the impact of the COVID‐19 pandemic period on the evolution of the disease in RomaniaThe exploratory spatial autocorrelation method is used to identify spatial hotspots of tuberculosis concentration in Romania [ABSTRACT FROM AUTHOR]

Inflammation is one of the suggested pathways linking exposure to neighbourhood air pollution and greenspace to psychological distress. Yet, inflammation has not been tested formally as a mediating factor in an adult human sample.Using data from 6813 adults participating in Understanding Society, the present study examines longitudinally the joint role of the two neighbourhood‐level exposures in psychological distress and the mediating role of inflammation. Annual concentrations of nitrogen dioxide and availability of greenspace in participants' neighbourhoods across England and Wales were examined. Psychological distress was measured with the General Health Questionnaire. Inflammation was assessed with C‐reactive protein and fibrinogen.A structural equation model path analysis showed that, in fully adjusted models, an increase in the amount of greenspace in one's neighbourhood was related to lower psychological distress 4 years later, but inflammation did not mediate this relationship.Neighbourhood‐level air pollution, specifically nitrogen dioxide, was not associated with psychological distress. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Spatiotemporal forecasting involves generating temporal forecasts for system state variables across spatial regions. Data‐driven methods such as Convolutional Long Short‐Term Memory (ConvLSTM) are effective in capturing both spatial and temporal correlations, but they suffer from error accumulation and accuracy loss as forecasting time increases due to the nonlinearity and uncertainty in physical processes. To address this issue, we propose to combine data‐driven and data assimilation (DA) methods for spatiotemporal forecasting. The accuracy of the data‐driven ConvLSTM model can be improved by periodically assimilating real‐time observations using the ensemble Kalman filter (EnKF) approach. This proposed hybrid ConvLSTM‐EnKF method is demonstrated through PM2.5 forecasting in China, which is a challenging task due to the complexity of topographical and meteorological conditions in the region, the need for high‐resolution forecasting over a large study area, and the scarcity of observations. The results show that the ConvLSTM‐EnKF method outperforms conventional methods and can provide satisfactory operational PM2.5 forecasts for up to 1 month with spatially averaged RMSE below 20 μg/m3 and correlation coefficient (R) above 0.8. In addition, the ConvLSTM‐EnKF method shows a substantial reduction in CPU time when compared to the commonly used NAQPMS‐EnKF method, up to three orders of magnitude. Overall, the use of data‐driven models provides efficient forecasts and speeds up DA. This hybrid ConvLSTM‐EnKF is a novel operational forecasting technique for spatiotemporal forecasting and is used in real spatiotemporal forecasting for the first time. Plain Language Summary: This study introduces an advanced method (ConvLSTM‐EnKF) for PM2.5 forecasting in China, which is a challenging task due to its large area coverage, and complex topographical and meteorological conditions. This innovative approach combines two techniques: one looks at historical data to make forecasts, while the other periodically incorporates new information from observations to improve forecasts over time. This combination significantly improves forecasting accuracy and provides reliable operational PM2.5 forecasts for up to 1 month. Notably, this method is more efficient than traditional approaches. Beyond air pollution, the method holds promise for improving predictions in other areas, including weather, climate, and environmental systems, marking a substantial step forward in our ability to anticipate and understand complex spatiotemporal phenomena. Key Points: A hybrid data‐driven (ConvLSTM) and data assimilation (EnKF) method is proposed for accurate and efficient spatiotemporal forecasting. A pre‐trained ConvLSTM is used for both the forecasting and assimilation processes, enabling fast online operational forecastingThe ConvLSTM‐EnKF demonstrates high efficiency by reducing CPU time by three orders of magnitude compared to the NAQPMS‐EnKF, a widely used air quality forecasting model in ChinaThe ConvLSTM‐EnKF enables online data assimilation (DA) for high‐dimensional systems and improves DA accuracy by allowing the use of a large ensemble size [ABSTRACT FROM AUTHOR]

Air pollution by particulate matter (PM) in the air including PM1.0, PM2.5, and PM10, which are categorized by particle size, is a critical global environmental issue, harming the climate, ecosystems, and human health. Especially, ultrafine dust including PM1.0 and PM2.5 poses significant human health risks. Commercial fabric‐based filters effectively trap PMs but cause high‐pressure drop and limited filter capacity and reusability. Electrospun nanofiber filters address some issues but have low mechanical strength, toxic exposure risks, long fabrication times, and restrained reusability. Herein, a reusable and transparent impaction‐based PM filter using a UV‐curable polymeric stencil with micro apertures is proposed. The polymeric stencil filters achieve high filter efficiency (68–94%), superior filter capacity, and low‐pressure drop (<64 Pa). The polymeric stencil filters can be easily cleaned with water or ethanol and remain stable under extreme temperatures (−196 to 450 °C) with slight shrinkage (0–7%). The polymeric stencil filters can be broadly utilized for not only industrial, indoor, and vehicle filters but also transparent and flexible facial health masks. [ABSTRACT FROM AUTHOR]

We apply a systems framework for analyzing the overall sustainability impacts of interventions to a case of the rice‐wheat cropping system of Punjab (India), where agricultural practices lead to air pollution‐related health impacts, over‐exploitation of groundwater, over‐use of fertilizers and reduced local crop diversity. We use this case to quantify how varying degrees of change in interventions result in sustainability impacts using an inclusive wealth‐based approach. We show that either improving the existing cropping system or inducing fundamental changes in the cropping system, can lead to substantial and wide‐ranging sustainability benefits. We also show that interventions that improve human health show the largest quantitative benefit due to the assumed high marginal value of human life. Accurate localized estimates of marginal values of stocks are needed for estimating overall sustainability impacts. Plain Language Summary: We use a systems‐based approach for studying air pollution as a challenge embedded in a broader network of sustainability issues, and analyze the cross‐sectoral impacts of policy interventions. We use the rice‐wheat cropping system in Punjab, India, as a case study, since agricultural practices in this system are associated with a number of inter‐linked sustainability challenges such as air pollution‐related health impacts, over‐exploitation of groundwater, over‐use of fertilizers and reduced local crop diversity. We analyze the sustainability impacts of varying degrees of policy‐induced change in this system and show that both incremental and fundamental changes can lead to wide‐ranging sustainability benefits. Key Points: We apply a systems framework for analyzing policy interventions to the rice‐wheat cropping system of Punjab (India)We quantify the sustainability impacts of interventions involving varying degrees of change in the system using an inclusive wealth‐based approachWe show how policy‐induced changes can lead to substantial and wide‐ranging sustainability benefits [ABSTRACT FROM AUTHOR]

This paper addresses the questions of what effect local regulations can have on pollutants with different lifetimes and how surface observations and remotely sensed data can be used to determine the impacts. We investigated the decadal trends of tropospheric sulfur dioxide (SO2) and aerosol pollution over Maryland and its surrounding states, using surface, aircraft, and satellite measurements. Aircraft measurements indicated fewer isolated SO2 plumes observed in summers, a ∼40% decrease of column SO2, and a ∼20% decrease of atmospheric optical depth ( AOD) over Maryland after the implementation of local regulations on sulfur emissions from power plants (∼90% reduction from 2010). Surface observations of SO2 and particulate matter ( PM) concentrations in Maryland show similar trends. OMI SO2 and MODIS AOD observations were used to investigate the column contents of air pollutants over the eastern U.S.; these indicate decreasing trends in column SO2 (∼60% decrease) and AOD (∼20% decrease). The decrease of upwind SO2 emissions also reduced aerosol loadings over the downwind Atlantic Ocean near the coast by ∼20%, while indiscernible changes of the SO2 column were observed. A step change of SO2 emissions in Maryland starting in 2009-2010 had an immediate and profound benefit in terms of local surface SO2 concentrations but a modest impact on aerosol pollution, indicating that short-lived pollutants are effectively controlled locally, while long-lived pollutants require regional measures. [ABSTRACT FROM AUTHOR]

Currently, the equivalent salt deposit density (ESDD) is adopted to characterise the pollution degree and many environmental pollution parameters are used to characterise air pollution. However, the relationships between ESDD and environmental pollution parameters are not clear. For the purpose of studying the influence of environmental factors such as haze-fog, high-conductivity fog on the insulators contamination and the equivalence between artificial salt fog and natural highly conductive pollutions, a parameter named equivalent soluble salt concentration (ESSC) was proposed to measure the soluble salt amount of the atmospheric air in this study. Based on the measuring principle, an ESSC measurement platform was developed. The developed platform was tested in the laboratory and used to measure the ESSC in the field. According to the experimental results, it was found that the soluble components deposited on the specimen surface in the field are the same as that in the atmospheric air nearby. As the pollution deposition is an accumulative process, it is expected that combined with ESDD, the ESSC can be used to predict the ESDD, which would be of significance to study the accumulated mechanism of insulator contamination, as well as to the external insulation design. [ABSTRACT FROM AUTHOR]

This paper describes how primary contaminants in ambient air affect the performance of the cathode in fuel cell electric vehicle applications. The effect of four atmospheric pollutants (SO2, NH3, NO2, and CO) on cathode performance was investigated by air impurity injection and recovery test under load. Electrochemical analysis via polarization and electrochemical impedance spectroscopy was performed for various concentrations of contaminants during the impurity test in order to determine the origins of performance decay. The variation in cell voltage derived empirically in this study and data reported in the literature were normalized and juxtaposed to elucidate the relationship between impurity concentration and performance. Mechanisms of cathode degradation by air impurities were discussed in light of the findings. [ABSTRACT FROM AUTHOR]

This study proposes a composite generation and transmission expansion planning (CGTEP) with high renewable penetration in Iran power grid. The goal is to minimise the generation and transmission investment and operation costs, start‐up/shut‐down costs, and the cost of energy not served during a 10‐year planning horizon. The problem is formulated in a mixed‐integer linear format, considering power flow limits, ramping rate limits, minimum up/down times of the generation units, uncertainty of wind and solar generations, and deterministic and probabilistic reliability constrains including reserve margins for generation capacities and expected energy not supplied boundary. In addition, according to the specific geographic characteristics of Iran, import and export of the electricity, as well as the limitation of water resources for power generation, is modelled in the CGTEP problem. The proposed CGTEP method is implemented in business as usual and renewable target cases without considering the uncertainty, and RT case with uncertainty considerations. In RT cases, 30% renewable penetration is targeted to reduce the CO2 emission by 10%. In addition, the capability of Iran power grid in providing green energy for the neighbouring countries is investigated in this study. [ABSTRACT FROM AUTHOR]

The article presents a list of publications published in several journals including "Air Pollutants: Effects on Forest Ecosystems," from the Acid Rain Foundation, "Plant Microbiology," by R. Campbell, "Sex in Nature," C. Catton and J. Gray, "The Elements of Graphing Data," edited by W. S. Cleveland and "Case Studies in Population Biology," edited L. M. Cook.

Increasing the model resolution is expected to be one way for improving air quality forecasts in urban areas. In this study, we evaluate the model performance in a large city at various resolutions to examine the best resolution for air pollution simulations. The comparison with measurements at a station near the traffic emissions shows the advantage of using high resolutions for capturing the extreme values. The statistical evaluation indicates that the highest model resolution (33 m) provides the best results for NOX concentration distributions near the traffic roads, while the improvement for roadside O3 with decreasing grid spacing stops at a certain point. The best model performance for the areas with a distance to the pollution sources is with the resolution of 100–300 m, at which the transport errors are equivalent to the emission biases. Plain Language Summary: As the increasing needs in the air quality forecasting in large cities, there is a trend in decreasing the model grid spacing to obtain more detailed pollutants distributions between neighborhoods or at street levels. To determine at which resolution the model can obtain the best representation of the pollutants' concentrations, we evaluate the model performance at different resolutions taking Hong Kong as an illustration. The analysis shows that the improvement with increasing model resolution is not monotonic for the areas far away from the intense emissions; however, the model with the highest resolution (33 m) reproduces the best results for the short‐lived species near the pollution sources. Key Points: Increasing horizontal resolution to 33 m improves the prediction of NOX near the traffic emissionsThe threshold of the model resolution is around 300 m for areas with a distance to the pollution sourcesThe changes of model performances with varied resolutions are different for NOX and O3 [ABSTRACT FROM AUTHOR]

Swine confinement buildings represent workplaces with high biological air pollution. It is suspected that individual components of inhalable air are causatives of chronic respiratory disease that are regularly detected among workers. In order to understand the relationship between exposure and stress, it is necessary to study the components of bioaerosols in more detail. For this purpose, bioaerosols from pig barns were collected on quartz filters and analysed via a combinatorial approach of 16S rRNA amplicon sequencing and metaproteomics. The study reveals the presence of peptides from pigs, their feed and microorganisms. The proportion of fungal peptides detected is considered to be underrepresented compared to bacterial peptides. In addition, the metaproteomic workflow enabled functional predictions about the discovered peptides. Housekeeping proteins were found in particular, but also evidence for the presence of bacterial virulence factors (e.g., serralysin‐like metalloprotease) as well as plant (e.g., chitinase) and fungal allergens (e.g., alt a10). Metaproteomic analyses can thus be used to identify factors that may be relevant to the health of pig farmers. Accordingly, such studies could be used in the future to assess the adverse health potential of an occupationally relevant bioaerosol and help consider defined protective strategies for workers. [ABSTRACT FROM AUTHOR]