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Debates about how best to support students' transition from school to university have re-emerged periodically since at least the 1970s. This paper focuses upon one aspect of this transition: how to develop the quantitative skills students acquire at school throughout the first year of their degree. We report on an attempt to inject pedagogic innovation into the teaching of quantitative methods to first-year geography undergraduates at a large Russell Group university in the UK. More specifically, we report on moving to a pedagogic approach of student-centred, inquiry-based learning, which uses quantitative methods to investigate the issue of air pollution. We explore whether "statistical anxiety" is still a common experience of undergraduate geographers and the extent to which pedagogical innovation can help to alleviate this. Although the focus is on UK Geography, the paper has wider relevance to anywhere geographical research methods are taught. [ABSTRACT FROM AUTHOR]

Owing to a long-term, extensive development model and inadequate industrial development planning, cases of atmospheric environment pollution frequently occur in Hebei province. By using such approaches as the Spatial Gini Coefficient, the Herfindahl–Hirschman Index and location entropy, this paper analyzes the spatial-distribution characteristics of atmospheric pollution-intensive industries such as the thermal power industry in Hebei province. As shown, atmospheric pollution-intensive industries, such as the thermal power industry in Hebei province, excessively cluster. As industrial agglomeration continuously intensifies, the spatial imbalance becomes increasingly prominent. Taking the number of days with excellent air quality as a benchmark, this paper divides prefecture-level cities of Hebei province into four types of industrial management and designs targeted strategies for the optimization of atmospheric pollution-intensive industries, such as the thermal power industry. In terms of policies, Type I and Type II cities are advised to strengthen the transfer of atmospheric pollution-intensive industries such as the thermal power industry, and Type III and Type IV cities are advised to improve capacities in atmospheric self-purification and green-technology innovation in a bid to help government departments to scientifically manage atmospheric pollution-intensive industries such as the thermal power industry. [ABSTRACT FROM AUTHOR]

Childhood and adolescence are critical periods for the development of the brain. However, a limited number of studies have explored how air pollution may associate with affective symptoms in youth. We performed a comprehensive review of the existing research on the associations between outdoor air pollution and affective disorders, suicidality, and the evidence for brain changes in youth. PRISMA guidelines were followed and PubMed, Embase, Web of Science, Cochrane Library, and PsychINFO databases were searched from their inception to June 2022. From 2123 search records, 28 papers were identified as being relevant for studying the association between air pollution and affective disorders (n = 14), suicide (n = 5), and neuroimaging-based evidence of brain alterations (n = 9). The exposure levels and neuropsychological performance measures were highly heterogeneous and confounders including traffic-related noise, indoor air pollution, and social stressors were not consistently considered. Notwithstanding, 10 out of the 14 papers provide evidence that air pollution is associated with increased risk of depression symptoms, and 4 out of 5 papers provide evidence that air pollution might trigger suicidal attempts and behaviors. Besides, 5 neuroimaging studies revealed decreased gray-matter volume in the Cortico-Striato-Thalamo-Cortical neurocircuitry, and two found white matter hyperintensities in the prefrontal lobe. Outdoor air pollution is associated with increased risks of affective disorders and suicide in youth, and there is evidence for associated structural and functional brain abnormalities. Future studies should determine the specific effects of each air pollutant, the critical exposure levels, and population susceptibility. • Air pollutants can induce neuroinflammation and oxidative stress in the CNS. • Air pollution (AP) increased the risks of depression and suicide in the juveniles. • Neuroimaging studies found AP-related brain structural and functional alterations. [ABSTRACT FROM AUTHOR]

The Fenwei Plain is listed as one of the most serious air pollution regions in China, along with Beijing-Tianjin-Hebei and Yangtze River Delta regions. This paper proposed a functional data analysis method to study the environmental pollution problem in the Fenwei Plain of China. Functional spatial autoregressive combined (FSAC) model with spatial autocorrelation of both the response variable and error term is developed. The model takes the SO2 concentration of Fenwei Plain as the dependent variable and the dew point temperature as the independent variable and realizes the maximum likelihood estimation using functional principal component analysis to obtain the asymptotic properties of parameter estimation and the confidence interval of the slope function. According to the findings of the empirical analysis of the Fenwei Plain, the SO2 concentration has significant seasonal characteristics and has decreased year over year for three years in a row. Winter is the season with the highest concentration on the Fenwei Plain, followed by spring and autumn, while summer is the season with the lowest concentration. Winter also has a high spatial autocorrelation. The FSAC model is more effective at fitting the concentration and dew point temperature of the Fenwei Plain in China because its mean square error (MSE) is significantly lower than that of the other models. As a result, this paper can more thoroughly study the pollution problem on the Fenwei Plain and offer guidance for prevention and control. [ABSTRACT FROM AUTHOR]

This paper argues that Kim Cho-yeop's science-fiction novel The Greenhouse at the End of the Earth (2020) reveals "trans-corporeal" material and ethical entanglement through its representation of a plant and a cyborg. The "mossvana" plant is genetically manufactured by a cyborg to resist and reduce the destructive effects of a human-induced environmental disaster. The plant is able to withstand the devastation of human-caused "dust," which decimates entire human, animal, and plant populations in the mid-twenty-first century. In this post-apocalyptic novel, the plant emerges not only as the nexus of human and non-human agencies, but also as a figure for human ethical commitments toward non-human others. Through tracing recent trends in the environmental humanities and building on Stacy Alaimo's thesis of "trans-corporeality," this paper argues that this post-pandemic novel reveals a new kind of ethics that fosters inter-species awareness and human-nonhuman connection. In the concluding section, I discuss air pollution in Korea and the metaphoric associations with breathing. I argue that breathing is also a "trans-corporeal" act that transports microscopic particles from the external environment into the human lungs and bloodstream. Breathing is a trans-corporeal act in both the material and ethical senses of the term. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling. [ABSTRACT FROM AUTHOR]

Accurate estimations and assessments of vehicle emissions can support decision-making processes. Current emission estimation tools involve several calculation methods that provide estimates of the exhaust components that result from driving on urban arterial roads. This is an important consideration, as the emissions generated have a direct impact on the health of pedestrians near the roads. In recent years, there has been an increase in the use of emission models, especially in combination with traffic simulator models. This is because it is very difficult to obtain an actual measurement of road emissions for all vehicles travelling along the analysed road section. This paper concerns a review of selected traffic simulations and the estimation of exhaust gas components models. The models presented have been aggregated into a group with respect to their scale of accuracy as micro, meso, and macro. This paper also presents an overview of selected works that combine both traffic and emission models. The presented literature review also emphasises the proper calibration process of simulation models as the most important factor in obtaining accurate estimates. This work also contains information and recommendations on modelling that may be helpful in selecting appropriate emission estimation tools to support decision-making processes for, e.g., road managers. [ABSTRACT FROM AUTHOR]

The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi. [ABSTRACT FROM AUTHOR]

The Environmental Protection Agency of the Republic of Serbia continuously monitors and collects air quality parameters at numerous measuring points throughout the country. The results revealed that the levels of air pollution recorded during the lockdown differed from those recorded during the same period the previous years. In this paper, we examined pollutant concentration trends in 2020 and a few previous years to determine the underlying causes of these trends. Pollutants are measured at five stations in Belgrade from the beginning of March to the end of July between 2015 and 2020. Because no hourly data were available, NO2, SO2, and suspended particles PM10 and PM2.5 are average daily values, whereas CO and ozone are daily 8-hour maximums. Concentrations were compared and averaged only when data for the same period over time was available, i.e. when comparing 2020 to previous years. The results were compared to the annual and daily limit values (for ozone and CO 8 hours maximum, respectively), as well as the variations in concentrations over time. The goal of calculating and displaying these values is to specifically identify a change in air pollution during the COVID-19 virus pandemic isolation period. In general, for Belgrade in 2020, NO2 concentrations are lower at four stations, PM2.5 concentrations are lower at two stations, PM10 concentrations are higher at one station and lower at another, and SO2 concentrations are higher than in previous years (2015-2019). [ABSTRACT FROM AUTHOR]

This paper introduces a cloud retrieval algorithm for the Geostationary Environmental Monitoring Spectrometer (GEMS), the first environmental geostationary orbit satellite, and validates its cloud products by comparing them with those produced by other instruments (OMI, TROPOMI, AMI, and CALIOP). The GEMS cloud products are corrected for the impact of clouds on the retrieval of atmospheric components using the O 2 –O 2 absorption band to retrieve the effective cloud fraction and cloud centroid pressure. The performance of the GEMS cloud retrieval algorithm was similar to that of the OMI. We analyzed the cloud retrieval characteristics for air pollution, typhoons, and sea fog in the East Asian region to evaluate whether GEMS cloud data can accurately represent various cloud features. Also, we evaluated the accuracy of the cloud retrieval algorithm through monthly validation for 2 years. The validation results provide a basis for future improvements of the GEMS cloud retrieval algorithm. [ABSTRACT FROM AUTHOR]

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively. [ABSTRACT FROM AUTHOR]

Lung health in Africa is of great public health concern. To exchange knowledge, communicate the latest scientific advances in lung health, build the capacity of young researchers, and provide a platform for networking of lung health stakeholders in Africa, the Pan African Thoracic Society (PATS), in partnership with the Respiratory Society of Kenya organized and held the 3rd Biennial PATS congress, from June 7–10, 2023 in Mombasa, Kenya. There were a total of 30 conference sessions, which included six skills building workshops, two research dissemination workshops, six plenary sessions, and five each of pediatric, adult, and thoracic surgery symposia. A total of 60 abstracts were presented, including 27 oral abstracts and 33 posters. A wide range of issues for lung health in Africa emerged. These include the emerging role of the environment, especially climate change and global warming, on lung health, the threat posed by the use of tobacco and other nicotine products in Africa, the high burden of lung infections, insecurity of medical oxygen supply systems, and high burden of chronic respiratory disease. In this paper, we summarize the proceedings of this conference and draw lessons that can be used to advance lung health in the African continent. [ABSTRACT FROM AUTHOR]

Valuation of the economic, social and environmental impacts of air pollution has become crucial for the benefitcost analysis of pollution restriction strategies, which serve as a foundation for establishing priorities for action. This paper focuses on the estimation of total external costs caused by road transport-related air pollutants using an integrated evaluation methodology combining air quality modelling, engineering science and economics. Emission factors and transport network characteristics were used to compute emissions from the road transport that is followed by economic valuation approaches adopted from international case studies and used for calculating the economic costs of air pollution in Türkiye. The results showed that total external costs of air pollution in Türkiye in 2018 ranged between 37,500 euros which is computed for CO emissions and 2,686 million euros computed as an upper limit for NOx emissions. Regarding the social costs of CO2 emissions, the values range between 31 million euro and 1,427 million euro, the former represents the low value estimate while the latter is the high value estimate. The findings indicate that the impact of emissions from road transport on environment and society can be substantial in Türkiye. Therefore, some regulations are necessary to reduce transport emissions and to sustain socio-economic welfare. [ABSTRACT FROM AUTHOR]

In recent years, the issue of air pollution has garnered significant public attention globally, with a particular emphasis on the challenge of atmospheric fine particulate matter (PM2.5) pollution. The efficient and precise simulation of changes in pollutant concentrations, as well as their spatial and temporal distribution, is essential for effectively addressing the air pollution issue. In this paper, the WRF-Chem model is used to simulate the meteorological elements including temperature (T), relative humidity (RH), wind speed (WS), and pressure (P), and the concentrations of PM2.5 and PM10 atmospheric pollutants in December 2020 in Xuzhou City. Simultaneously, the ADMS-Urban model was employed to conduct a higher spatial resolution study of PM2.5 concentrations during the heavy pollution days of 11–12 December 2020 in Xuzhou City. The study shows that the WRF-Chem model can simulate the meteorological conditions of the study time period better, and the correlation coefficients (R) of pressure, temperature, wind speed, and relative humidity are 0.99, 0.87, 0.75, and 0.70, respectively. The WRF-Chem model can accurately simulate the PM2.5 concentration on clean days (R of 0.66), but the simulation of polluted days is not satisfactory. Therefore, the ADMS-Urban model was chosen to simulate the PM2.5 concentration on polluted days in the center of Xuzhou City. The ADMS-Urban model can simulate the distribution characteristics and concentration changes of PM2.5 around roads and buildings in the center of Xuzhou City. Comparing the simulation results of the two models, it was found that the two models have their own advantages in PM2.5 concentration simulation, and how to better couple the two models is the next research direction. [ABSTRACT FROM AUTHOR]

Particulate Matter (PM) air pollution has been linked to major adverse health effects. Road transport still contributes significantly to ambient PM concentrations, but mainly due to the non-exhaust emissions from vehicles. For the first time worldwide, limits for non-exhaust emissions have been proposed by the European Union for the upcoming Euro 7 step. For these reasons, interest in brake emissions has increased in the past few years. Realistic emission factors are necessary to accurately calculate the contribution of brake emissions to air pollution but also to estimate the emissions reduction potential of new or existing technologies and improved brake formulations. This paper reviews emission factors from light-duty vehicles reported in the literature, with a focus on those that followed the recently introduced Global Technical Regulation (GTR 24) methodology on brakes in light-duty vehicles. Reduction efficiencies of non-asbestos organic (NAO) pads, brake dust filters, ceramic discs, coated discs, and regenerative braking are also discussed. Finally, the emission factors are compared with roadside measurements of brake emissions and emission inventories worldwide. The findings of this study can be used as an input in emission inventories to estimate the contribution of brakes to air pollution. [ABSTRACT FROM AUTHOR]

Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers' desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare. [ABSTRACT FROM AUTHOR]

The intensive exploitation and usage of fossil fuels has led to serious environmental consequences, including soil, water, and air pollution and climate changes, and it has compromised the natural resources available for future generations. In this context, identifying new energy storage technologies can be considered a sustainable solution to these problems, with potential long-term effects. In this work, were analyzed different alternatives that can be suitable for replacing non-renewable sources, where hydrogen, wave, wind, or solar energies were considered. Although they have numerous advantages in terms of usage and substantially reducing the environmental impact, this paper is focused on lithium-ion batteries, whose high performance and safety during operation have made them attractive for a wide range of applications. The study of potential replacement technologies and the technical requirements for the main materials used is the starting point in reducing the environmental footprint, without affecting the technical capabilities, followed by the transition toward economic circularity and climate neutrality. [ABSTRACT FROM AUTHOR]

In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often "black boxes", which represents an extra challenge as it limits end-users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive data set open to the public, it was designed to provide a long-term evaluation of the precision, accuracy, and stability of commercially available sensor systems. This overview discusses the assessment methodology, and key findings showcasing the significance of the study. The results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-users data challenges are presented. Offering key information about the sensor systems' capabilities the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution. [ABSTRACT FROM AUTHOR]

This paper aims to improve and extend the improved spatial Moran's I theory by analyzing multi-observation samples. By constructing an expanded spatial weight matrix, a vector definition of the improved spatial Moran's I is given. In order to improve the judgment basis of the improved spatial Moran's I, the range of the improved spatial Moran's I is derived using the non-negativity of variance. Since the improved Moran's I is only applicable to the analysis of a single variable with unknown distribution, a Moran's I matrix suitable for analyzing the spatial autocorrelation of multiple variables is proposed. The distribution of the elements of the Moran's I matrix is studied by Monte Carlo simulation. The simulation results show that only the elements on the non-main diagonal follow a normal distribution when the sample size is small. Any element follows a normal distribution when the sample size is large. Then it is proved that the Moran's I matrix follows a Wishart distribution when the spatial weight matrix is a positive definite matrix. Finally, several comprehensive evaluation indicators suitable for the theory of multivariate spatial autocorrelation are proposed based on the algebraic meaning of the Moran's I matrix. Spatial autocorrelation analysis is carried out in combination with multi-dimensional air pollution data. [ABSTRACT FROM AUTHOR]

Atmospheric Boundary Layer (ABL) height variability, along with meteorological parameters and pollutant concentrations, are studied during different months in Mumbai region for the period of November 2020 to October 2021. ABL height is measured using a monostatic SODAR (SOnic Detection And Ranging) system. The capabilities, behaviour, and benefits of employing this ground-based remote sensing system in Mumbai are examined in this preliminary report after a year of SODAR monitoring. SODAR dataset is also compared with Radiosonde input ERA5 reanalysis data. The ventilation coefficient (VC) analysis for the same time period is also covered in the paper. Especially in the months of November, December, January, February, August, September, and October, the ABL height often approached 700 m. The ABL height is increased to 1000–1200 m in April because of the strong winds and warm weather. The month of April has the greatest monthly average height (418 m), while the month of February has the lowest monthly average height (179 m). In April, when the wind speed is 7.2 m/s, the convection duration is observed to be the longest, while in December, when the wind speed is 1.75 m/s, it is the shortest. Research highlights: For the first time, real-time hourly atmospheric boundary layer height data measured from SODAR system are analysed over the Mumbai region. The ABL height often reached 700 m, especially in the months of November, December, January, February, August, September, and October. Due to the high wind and warmth in April, the ABL height is extended to 1000–1200 m. The highest ventilation coefficient values around 7095 m2/s are recorded in the month of April, while the lowest values around 2912 m2/s are recorded in the months of November and December. [ABSTRACT FROM AUTHOR]

As air pollution becomes more and more serious, PM2.5 is the primary pollutant, inevitably attracts wide public attention. Therefore, a novel PM2.5 concentration forecasting method based on linear fuzzy information granule_dynamic time warping_hierarchical clustering algorithm (LFIG_DTW_HC algorithm) and generalized additive model is proposed in this paper. First, take 30 provincial capitals in China for example, the cities are divided into seven regions by LFIG_DTW_HC algorithm, and descriptive statistics of PM2.5 concentration in each region are carried out. Secondly, it is found that the influencing factors of PM2.5 concentration are different in different regions. The input variables of the PM2.5 concentration forecasting model in each region are determined by combining the variable correlation with the generalized additive model, and the main influencing factors of PM2.5 concentration in each region are analyzed. Finally, the empirical analysis is conducted based on the input variables selected above, the generalized additive model is established to forecast PM2.5 concentration in each region, the comparison of the evaluation indexes of the training set and the test set proves that the novel PM2.5 concentration forecasting method achieves better prediction effect. Then, the generalized additive model is established by selecting cities from each region, and compared with the auto-regressive integrated moving average (ARIMA) model. The results show that the novel PM2.5 concentration forecasting method can achieve better prediction effect on the premise of ensuring high accuracy. [ABSTRACT FROM AUTHOR]

Air pollution has become a major global concern with profound implications for public health worldwide. Among the most significant air pollutants are particulate matter, sulfur dioxide, nitrogen oxides, heavy metals, and ammonia, which have serious and wide-ranging effects on human health. Respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and pneumonia, are frequently associated with air pollution, while the risk of developing other serious conditions such as diabetes and cardiovascular problems is also elevated. Of particular concern is the exposure of children to air pollution, as their immune systems are not fully developed, and they spend more time outdoors, where pollution concentrations are often higher. In Serbia, as in many other countries, integrated intersectoral measures are necessary to reduce air pollution and protect the health of citizens. This paper aims to provide a basic overview of air pollution issues in Serbia and explore control and measurement measures to better understand the fundamental concepts and make progress in addressing this serious problem. [ABSTRACT FROM AUTHOR]

Green roofs (GRs) are one of the best practices that researchers are promoting due to environmental, social, economic, and ecological benefits. This bibliometric review on GRs performs a comprehensive evaluation where shortlisted studies are categorized into empirical and nonempirical. A review of the nonempirical studies affirms the importance of GRs, and the appraisal of the empirical studies helps understand the performance evaluation methods. The performance of GRs largely depends on the overall assembly configuration, especially the vegetation and substrate layers, along with the site and settings. Such studies are conducted through field and laboratory experiments, simulation modeling, mathematical models, or combination methods. These methods help understand the thermal, hydrological and energy performance, urban heat island (UHI) mitigation, noise reduction, air pollution reduction, sustainability, and retrofitting potentials of GRs. This paper presents a comprehensive review of the performance evaluation methods to understand their association with performance parameters and variables. This will help identify an effective performance evaluation method for an inclusive evaluation of any GR assembly. [ABSTRACT FROM AUTHOR]

The Balearic Islands (Spain) passed a regional law in 2019 banning the registration of diesel and petrol vehicles from 1 January 2025 and 1 January 2035, respectively. This law was heavily criticised before and during its implementation and was finally abolished after six months. This paper analyses the effect of this law on the registrations of both types of vehicles, as well as the registration of non-polluting vehicles. Both the synthetic control method and the difference-in-difference estimator show that the ban had no impact on any type of vehicle, not even on those that are non-polluting. Although the Balearic Islands' regional government sought to pioneer implementation of this policy, the incorrect specification of the law, the uncertainty surrounding its approval and its short-term effects and may have reduced its effectiveness. [ABSTRACT FROM AUTHOR]

BACKGROUND: The prenatal environment influences lifetime health; epigenetic mechanisms likely predominate. In 2016, the first international consortium paper on cigarette smoking during pregnancy and offspring DNA methylation identified extensive, reproducible exposure signals. This finding raised expectations for epigenome-wide association studies (EWAS) of other exposures. OBJECTIVE: We review the current state-of-the-science for DNA methylation associations across prenatal exposures in humans and provide future recommendations. METHODS: We reviewed 134 prenatal environmental EWAS of DNA methylation in newborns, focusing on 51 epidemiological studies with meta-analysis or replication testing. Exposures spanned cigarette smoking, alcohol consumption, air pollution, dietary factors, psychosocial stress, metals, other chemicals, and other exogenous factors. Of the reproducible DNA methylation signatures, we examined implementation as exposure biomarkers. RESULTS: Only 19 (14%) of these prenatal EWAS were conducted in cohorts of 1,000 or more individuals, reflecting the still early stage of the field. To date, the largest perinatal EWAS sample size was 6,685 participants. For comparison, the most recent genome-wide association study for birth weight included more than 300,000 individuals. Replication, at some level, was successful with exposures to cigarette smoking, folate, dietary glycemic index, particulate matter with aerodynamic diameter <10 μm and <2.5 μm, nitrogen dioxide, mercury, cadmium, arsenic, electronic waste, PFAS, and DDT. Reproducible effects of a more limited set of prenatal exposures (smoking, folate) enabled robust methylation biomarker creation. DISCUSSION: Current evidence demonstrates the scientific premise for reproducible DNA methylation exposure signatures. Better powered EWAS could identify signatures across many exposures and enable comprehensive biomarker development. Whether methylation biomarkers of exposures themselves cause health effects remains unclear. We expect that larger EWAS with enhanced coverage of epigenome and exposome, along with improved single-cell technologies and evolving methods for integrative multi-omics analyses and causal inference, will expand mechanistic understanding of causal links between environmental exposures, the epigenome, and health outcomes throughout the life course. [ABSTRACT FROM AUTHOR]

Background: The occurrence of orofacial Clefts (OFCs) is a congenital disease caused by many factors. According to recent studies, air pollution has a strong correlation with the occurrence of OFCs. However, there are still some controversies about the current research results, and there is no relevant research to review the latest results in recent years. Objective: In this paper, the authors conducted a systematic review and meta-analysis to explore the correlation between ambient air pollution and the occurrence of neonatal OFCs deformity. Methods: We searched Pubmed, Web of science, and Embase databases from the establishment of the database to May 2023. We included observational studies on the relationship between prenatal exposure to fine particulate matter 2.5 (PM2.5), fine particulate matter 10 (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO) and the risk of cleft lip (CL), cleft palate (CP), cleft lip with or without palate (CL/P). the Newcastle-Ottawa quality assessment scale (NOS) was used to evaluate the quality of the literature. Funnel plot and Egger's regression were used to verify the publication bias. Random effect model or fixed effect model was used to estimate the combined relative risk (RR) and 95% confidence interval (95%CI). Results: A total of eleven studies were included in this study, including four cohort studies and seven case-control studies, including 22,453 cases of OFCs. Ten studies had low risk of bias and only one study had high risk of bias. Three studies reported that PM2.5 was positively correlated with CL and CP, with a combined RR and 95%CI of 1.287(1.174,1.411) and 1.267 (1.105,1.454). Two studies reported a positive correlation between O3 and CL, with a combined RR and 95%CI of 1.132(1.047,1.225). Two studies reported a positive correlation between PM10 and CL, with a combined RR and 95%CI of 1.108 (1.017,1.206). No association was found between SO2, CO, NO2 exposure during pregnancy and the risk of OFCs. Conclusion: The results of this study showed that there was a significant statistical correlation between exposure to PM10, PM2.5, O3 and the risk of OFCs in the second month of pregnancy. Exposure assessment, research methods and mechanisms need to be further explored. [ABSTRACT FROM AUTHOR]

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM2.5) due to their low cost, wide coverage, and all-weather capability. Estimation of PM2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons (p < 0.01) and different spatial resolutions (p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions. [ABSTRACT FROM AUTHOR]

Air pollution is a leading cause of human diseases. Accurate air quality predictions are critical to human health. However, it is difficult to extract spatiotemporal features among complex spatiotemporal dependencies effectively. Most existing methods focus on constructing multiple spatial dependencies and ignore the systematic analysis of spatial dependencies. We found that besides spatial proximity stations, functional similarity stations, and temporal pattern similarity stations, the shared spatial dependencies also exist in the complete spatial dependencies. In this paper, we propose a novel deep learning model, the spatiotemporal adaptive attention graph convolution model, for city-level air quality prediction, in which the prediction of future short-term series of PM2.5 readings is preferred. Specifically, we encode multiple spatiotemporal dependencies and construct complete spatiotemporal interactions between stations using station-level attention. Among them, we design a Bi-level sharing strategy to extract shared inter-station relationship features between certain stations efficiently. Then we extract multiple spatiotemporal features with multiple decoders, which it is extracted from the complete spatial dependencies between stations. Finally, we fuse multiple spatiotemporal features with a gating mechanism for multi-step predictions. Our model achieves state-of-the-art experimental results in several real-world datasets. [ABSTRACT FROM AUTHOR]

Air pollution is an important cause of adverse health effects even in the Nordic countries, which have relatively good air quality. Modelling-based air quality assessment of the health impacts relies on reliable model estimates of ambient air pollution concentrations, which furthermore rely on good quality spatially resolved emission data. While quantitative emission estimates are the cornerstone of good emission data, description of the spatial distribution of the emissions is especially important for local air quality modelling on high resolution. In this paper we present a new air pollution emission inventory for the Nordic countries with high-resolution spatial allocation (1 km × 1 km) covering years 1990, 1995, 2000, 2005, 2010, 2012, and 2014. The inventory is available at https://doi.org/10.5281/ZENODO.8070155 (Paunu et al., 2023)). To study the impact of applying national data and methods to the spatial distribution of the emissions, we compared road transport and machinery and off-road sectors to CAMS-REGv4.2, which used consistent spatial distribution method throughout Europe for each sector. Road transport is a sector with well-established proxies for spatial distribution, while for machinery and off-road the choice of proxies is not as straightforward as it includes a variety of different type of vehicles and machines operating in various environments. We found that the CAMS-REGv4.2 was able to produce similar spatial patterns as our Nordic inventory for the selected sectors. However, the resolution of our Nordic inventory allows for more detailed impact assessment than the CAMS-REGv4.2, which had resolution of 0.1° × 0.05° (longitude-latitude, roughly 5.5 km × 3.5-6.5 km in the Nordic countries). The EMEP/EEA Guidebook chapter on spatial mapping of emissions has recommendations for the sectoral proxies. Based on our analysis we argue that the guidebook should have separate recommendations for proxies for several sub-categories of the machinery and off-road sectors, instead of including them within broader sectors. We suggest that land use data is the best starting point for proxies for many of the subsectors, and they can be combined with other suitable data to enhance the spatial distribution. For road transport, measured traffic flow data should be utilized where possible, to. support modelled data in the proxies. [ABSTRACT FROM AUTHOR]

The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of -2.38 % and -2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO 2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored how urbanization (urban heat island effect and air pollution) affects urban tree growth. The radial growth and xylem anatomical characteristics of trees in urban and rural areas of Harbin were compared by means of tree-ring anatomy. Results showed that there were significant differences in the growth of both broadleaf trees and conifers between urban and rural areas. Urbanization may have the effect of slowing down tree growth, and conifers may be more sensitive to urbanization. Warming and drying climate in Harbin may be an important factor affecting tree growth. Our findings provide a theoretical basis for the future evaluation of the effect of urbanization on tree growth and ecological benefits, as well as the selection of tree species in urban forests. In the context of the intensification of global urbanization, how urbanization (urban heat island effect and air pollution) affects urban tree growth is not fully understood. In this paper, the radial growth and xylem anatomical characteristics of three different tree species (Quercus mongolica, Fraxinus mandshurica, and Pinus sylvestris var. mongolica) in urban and rural areas of Harbin were compared by means of tree-ring anatomy. The results showed that there were significant differences in the growth of both broadleaf trees and conifers between urban and rural areas. The vessel number, cumulative area of vessels, and theoretical hydraulic conductivity of all tree species in rural areas were higher than those in urban areas, indicating that urbanization may have the effect of slowing down growth. However, broadleaf trees in urban areas had higher vessel density and a greater percentage of a conductive area within xylem and theoretical xylem-specific hydraulic conductivity. The thickness of cell walls and cell wall reinforcement index of P. sylvestris var. mongolica were strongly reduced by air pollution, implying that it may be more sensitive to urbanization. Compared to Q. mongolica, F. mandshurica showed less sensitivity to urbanization. Warming and drying climate in Harbin may be an important factor affecting tree growth. [ABSTRACT FROM AUTHOR]

Ensuring food security is a major strategic issue and a key to maintaining social stability for China. China’s grain produc‑ tion may be adversely affected by both ground-level ozone and high temperature due to the rise in ozone concentration and extreme weather. A large amount of literature has studied the effects of ground-level ozone and high temperature on crop yield separately, but very few of them have investigated the joint effects of these two factors on crop yield. We collected various types of air pollutants and climate-related variables from ECMWF reanalysis data. Combined with the input-output data and other economic data at the county level, a seven-year panel dataset with 1747 counties in China was constructed. This paper first used semi-parametric estimation methods to estimate the separate and joint nonlinear impacts of ground-level ozone and high temperature on crop yield, respectively. Our results suggested that high ozone concentration and high temperature had significant adverse effects on rice and corn, respectively. The nonlinear estimation results showed that the critical loss points of rice to ozone and air temperature were about 53×10−9 and 19 ℃, while these numbers were approximately 60×10−9 and 18 ℃, respectively, for corn. The bivariate semi-parametric estimation results revealed that these two crops were affected by ozone and high temperature differently: rice was sensitive to both ozone and high temperature, but corn was more sensitive to high temperature than ozone. Empirical results remained robust after a series of robustness tests. Given that the background level of ozone in China has been increasing over the years, our nonlinear estimation results suggest that it is an economical choice to restrict the ozone levels to below 65×10−9 and 60×10−9, respectively, in rice- and corn-grown regions, balancing between grain loss and the cost of controlling ozone pollution. In addition, our binary semi-parametric estimation results suggest that the department of agriculture should fully consider the sensitivity heterogeneity of different crops to ozone and high temperature, and guide farmers to adapt via crop choices when other conditions permit to reduce or eliminate the negative impact of ozone on grain production. [ABSTRACT FROM AUTHOR]

Urban air pollution has become a regional environmental problem. In order to explore whether island areas were affected by the urban development of surrounding areas, in this paper, we systematically study the vertical distribution characteristics of atmospheric components, meteorological drivers, potential pollution sources, and the population health risks of fine particulate matter in island cities in China. The vertical profiles of three atmospheric pollutants (aerosols, NO2, and HCHO) in the lower troposphere of Huaniao Island in the East China Sea (ECS) were obtained using ground-based multi-axial differential optical absorption spectroscopy (MAX-DOAS). The results show that the aerosol extinction coefficients, NO2, and HCHO were primarily distributed at altitudes below 1 km, and the atmospheric pollutants in Zhoushan were obviously affected by high-altitude transfer. The main meteorological driving factors of aerosols, NO2, and HCHO were different at different altitudes. The key factor contributing to the high column concentrations of NO2 and HCHO in the upper air (greater than 400 m) was the transport of pollutants brought about by changes in wind speed. By exploring the main potential sources of atmospheric pollutants, it was found that the main sources of aerosols, NO2, and HCHO are coastal cities in the Yangtze River Delta, including southeast Zhejiang Province, southeast Fujian Province, Shanghai, ECS, and the Yellow Sea. Compared with aerosols and HCHO, local primary emissions are an important source of NO2, which are mainly related to industrial activities in Zhoushan Port. In addition, using the expose-response function model, the number of attributable cases of PM2.5 air pollution in Zhoushan City in 2019 accounted for 6.58% of the total population. This study enriches our understanding of the vertical distribution characteristics of atmospheric composition and health risk assessment on Chinese islands. [ABSTRACT FROM AUTHOR]

Complex mixtures of substances are in the atmosphere and they can cause diseases in humans and biological communities after acute or chronic exposition. This paper focuses on the physical measurement of particulate matter, a proxy for air pollution, and a biological method for mutation assessment due to plants' exposure to air pollution. The objective of this research was to characterize the air pollution seasonality in municipalities in southern Brazil, and also to understand the relation between air pollution and the biological response of the Tradescantia sp. clone 4430. The optical sensor SDS011 was used for measurements of particulate matter (PM) and the Trad-SHM bioassay was chosen to quantify the mutagenic alterations that occurred in stamen hairs during the study period, with PM data being measured every 5 seconds and the flowers being harvested approximately every two weeks for laboratory analysis. The Pearson test was applied to verify the correlation between PM and mutations in stamen hair as a result of which it was observed that there is a positive correlation between these data, with the highest value found being r = 0.61. Also, the period with the highest occurrence of pink cells was between autumn and spring, the same period in which an unusual increase in PM concentrations was also observed, a period that corresponds to a less favorable dispersion of pollutants in the atmosphere. The use of Tradescantia sp. clone 4430 showed sensitivity to the environments in which it was exposed. Biomonitoring is an important tool for understanding the effects of pollutants on the ecosystem. [ABSTRACT FROM AUTHOR]

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]

In recent years, air pollution in Xinjiang, the core region of the Silk Road Economic Belt, has become increasingly severe, posing a more significant threat to human health. This paper selects the tropospheric ozone, nitrogen dioxide, and formaldehyde datasets under OMI remote-sensing monitoring and the PM2.5 dataset in China High Air Pollutants (CHAP) for 2018–2021. The spatial and temporal distribution of multi-pollutants, the spatial autocorrelation of Moran's I index pollutants, and the correlation between pollutants in the warm period were studied in southern Xinjiang. Meanwhile, the geographical and temporal weighted regression (GTWR) model was used for influencing factor analysis, and the BenMap-CE model was used for health benefit analysis. The results showed that the spatial distribution of ozone concentration values in southern Xinjiang shows a decreasing distribution pattern from the east–central region to the western and southern regions. The spatial distribution of formaldehyde concentration values is opposite to that of ozone. There is a clear high-value area in the ozone concentration value in April–September. The NO2 column concentration values were in the range of 0.55~1.09 × 1015 molec/cm2 in most parts of southern Xinjiang. The area of high concentration values is located in the northeast of the study area; PM2.5 concentration values are higher in the middle area of southern Xinjiang. The spatial autocorrelation characteristics showed that the spatial aggregation of O3 and NO2 displayed a slow increasing trend year by year. The spatial aggregation of HCHO and PM2.5 fluctuated slightly in four years. The overall trend of HCHO is slowly decreasing, while PM2.5 is fluctuating and increasing. In the GTWR model analysis, overall, the atmospheric pressure has a strong influence on all pollutants. The effect of NO2 on O3 was higher than that of HCHO among the four pollutants. The correlation between O3 and PM2.5 was as high as −0.7872. The BenMap-CE health benefits assessment concluded that the number of premature deaths caused by ozone pollution was much higher than that of premature deaths caused by PM2.5. The highest number of premature deaths for both pollutants occurred in Kashgar. [ABSTRACT FROM AUTHOR]

During the "Parade Blue" period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the "Parade Blue" period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the "Parade Blue" period, and the effectiveness of the source control strategy was proved. [ABSTRACT FROM AUTHOR]

Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution. The Washington passive sampler (WPS), an ultra-low-cost method for measuring the long-term average levels of light-absorbing carbon (LAC) air pollution, uses digital images to measure the changes in the reflectance of a passively exposed paper filter. A prior publication on WPS reported high precision and reproducibility. Here, we deployed three methods to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir for PM2.5; two ultrasonic personal aerosol samplers (UPAS) with quartz filters for the thermal-optical analysis of elemental carbon (EC); and two WPS for LAC. We compared multiple rounds of 4-week-average measurements. The analyses calibrating the LAC to the elemental carbon measurement suggest that 1 µg of EC/m3 corresponds to 62 PI/month (R2 = 0.83). The EC-LAC calibration curve indicates an accuracy (root-mean-square error) of 3.1 µg of EC/m3, or ~21% of the average elemental carbon concentration. The RMSE values observed here for the WPS are comparable to the reported accuracy levels for other methods, including reference methods. Based on the precision and accuracy results shown here, as well as the increased simplicity of deployment, the WPS may merit further consideration for studying air quality in homes that use solid fuels. [ABSTRACT FROM AUTHOR]

Abstract: Recycling of paper and glass from household waste is an integrated part of waste management in Denmark, however, increased recycling is a legislative target. The questions are: how much more can the recycling rate be increased through improvements of collection schemes when organisational and technical limitations are respected, and what will the environmental and economic consequences be? This was investigated in a case study of a municipal waste management system. Five scenarios with alternative collection systems for recyclables (paper, glass, metal and plastic packaging) were assessed by means of a life cycle assessment and an assessment of the municipality’s costs. Kerbside collection would provide the highest recycling rate, 31% compared to 25% in the baseline scenario, but bring schemes with drop-off containers would also be a reasonable solution. Collection of recyclables at recycling centres was not recommendable because the recycling rate would decrease to 20%. In general, the results showed that enhancing recycling and avoiding incineration was recommendable because the environmental performance was improved in several impact categories. The municipal costs for collection and treatment of waste were reduced with increasing recycling, mainly because the high cost for incineration was avoided. However, solutions for mitigation of air pollution caused by increased collection and transport should be sought. [Copyright &y& Elsevier]

The market-oriented reform of China's power market has gradually transformed power prices from government pricing to market regulation, which not only promotes the production efficiency of industrial enterprises, but also inhibits the excessive consumption and waste of power by residential power users. This paper uses the data from 2006–2018 combined with the precious industrial power price data and macroeconomic data of 100 cities in China, takes the marketization reform of the power market in 2015 as a quasi-natural experiment, and uses the difference-in-differences model to empirically study the causal relationship between power market reform and air pollution for the first time. The study found that power market reform can reduce air pollution, and this conclusion is also supported by a number of robustness tests. Mechanism analysis shows that power market reform can reduce air pollution by improving power market efficiency, promoting technological progress, and reducing power consumption. Heterogeneity analysis shows that power market reform can suppress air pollution more significantly in eastern regions, regions with severe air pollution, and regions with larger populations. This paper not only provides new research perspectives and research ideas for air pollution prevention and control, but also provides empirical evidence for the positive externalities of power market reform. [ABSTRACT FROM AUTHOR]

Reducing air pollution is a major policy challenge, especially in densely populated urban areas where human exposure to emissions is considerable. This paper develops and examines a series of scenarios for the evolution of transport-related emissions in the area of Santiago, Chile. The study spans a time window up to 2050 and focuses on seven air pollutants and CO2. It compares a reference scenario with policy counterfactual scenarios involving a rapid electrification of public buses, a carbon pricing scheme and a kilometer charge differentiated by vehicle type. The reference scenario predicts a 55–80% reduction in air pollutants emitted by urban transport systems, by 2050. The corresponding reduction of CO2 lies at 6%, highlighting the asymmetric evolution in tailpipe filtering and carbon capture technologies. The analysis suggests that ramping up the efforts to electrify the bus fleet may eliminate 25% of the CO2 and at least 10% of the remaining air pollutant emissions in 2050. These figures increase to 45 and 30%, respectively, if rapid electrification is accompanied by one of the two tax schemes. With a distinct program to recycle the revenue from the tax schemes, the policy reform can be welfare improving for all groups and its environmental objectives can be achieved without adverse distributional consequences. The paper highlights the synergies of policies curbing climate change and tacking air pollution from the viewpoint of urban transport. [ABSTRACT FROM AUTHOR]

In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

Traffic and production restrictions are two important emergency measures for controlling urban air pollution. The lockdown policies implemented during the COVID-19 pandemic period are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Taking Wuhan, China as the study area, this paper firstly verified the changes in six air pollutants and analyzed their change rules in different lockdown periods using statistical methods. Then the structural breakpoints in air pollutants were detected via regression discontinuity design model. To comprehensively understand the effects of restrictions on air pollution, the influences of meteorological conditions on air pollution were also investigated. The results illustrated that the concentrations of PM2.5, PM10 and NO2 decreased significantly during lockdown period. By comparing with the RDD coefficients of PM2.5 (−34.46), PM10 (−37.11) and NO2 (−19.15), the lockdown had little effect on CO (−0.32). The traffic and production restrictions had no apparent effects on SO2. Although O3 showed an increasing trend, the increase was not limited to the lockdown period, meaning that the traffic and production restrictions had less effect on the increasing trend of O3 concentration. Moreover, the structural breakpoints were verified in four air pollutants (PM2.5, PM10, NO2, and CO), and the structural breakpoints were caused by lockdown instead of the Spring Festival. The results also indicated that the meteorological conditions were not the main reasons for the changes in air pollutants during the lockdown period. This paper reveals how the traffic and production restrictions affect urban air pollution and provides a strong implementation basis for the air pollution control policy. Implications: The traffic and production restrictions are two important emergency measures for controlling heavy urban air pollution. However, these two measures have never been implemented in a large area like a city for a long enough period, so the effectiveness of these two measures has never been estimated quantitatively at a city level. The lockdown policies implemented during the COVID-19 pandemic are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Thus, this study measured the effectiveness of production and traffic restrictions on different air pollutants. This study provides the following implications: (1) the dominant factors for air pollution changes during the lockdown are traffic and production restriction instead of meteorological conditions; (2) the production and traffic restriction policies are effective for reducing concentrations of PM2.5, PM10 and NO2, while having less effect on O3 and CO concentrations; (3) the sharp changes in air pollutants in 2020 are unlikely to be caused by the Spring Festival. These findings are crucial for making more comprehensive policies for protecting urban air quality. [ABSTRACT FROM AUTHOR]

While the problem of Delhi's air pollution is well recognized, as is the class bias of the judiciary in targeting certain sources of air pollution over others, what is less will understood are the unintended consequences of air pollution interventions on indoor and outdoor air quality. This paper shows how the National Green Tribunal has targeted one source of air pollution in recommending waste-to-energy technologies that deprive waste pickers of their livelihoods. The pandemic-related lockdown provides an opportunity to assess the impacts of livelihood deprivation on fuel use. Using data collected from surveys of waste pickers, this paper shows that decreases in income are related to an increase in reliance on solid fuels. Therefore, we argue that air pollution interventions designed to control air pollution from poor waste management have the unintended consequence of increasing air pollution from a different source (biomass burning for cooking) that specifically impacts waste pickers by increasing their exposure to indoor air pollution, while also undermining the government's urban air pollution mitigation efforts overall. [ABSTRACT FROM AUTHOR]

Air pollution health effects in New Zealand were first assessed in the Health and Air Pollution in New Zealand (HAPINZ 1.0) study undertaken by Fisher et al (2007) for 2001. This work was later updated for 2006 by Kuschel et al (2012) (HAPINZ 2.0), using monitoring data collected to show compliance with a national environmental standard for particulate matter (PM10) (MfE 2011). At the time, limited data meant that HAPINZ 2.0 used PM10 as a proxy for all air pollution, and impacts were assigned to sources using their contributions to PM10 (rather than all) emissions. On this basis, the findings prioritised addressing domestic fire emissions over motor vehicle emissions in most locations across New Zealand. The authors acknowledged that HAPINZ 2.0 likely under-estimated effects due to motor vehicle nitrogen dioxide (NO2). This was flagged for future investigation. In 2019, the Ministry for the Environment, Waka Kotahi, Te Manatū Waka Ministry of Transport and Ministry of Health commissioned a new update - HAPINZ 3.0 - to update the air pollution impacts for 2016. HAPINZ 3.0 developed New Zealand specific exposure-response functions for a range of health outcomes, including childhood asthma, from exposure to both fine particulate (PM2.5) and NO2 using a two pollutant model. While some contribution from NO2 was expected, the extent of the impact was startling. The health burden from all human-generated (anthropogenic) sources of air pollution was estimated at $15.6 billion, with NO2 exposure accounting for just over 60%. At the time the initial findings came to light, no other researchers had found such strong associations between NO2 and mortality. This paper reviews the key features and findings of HAPINZ 3.0. Possibly more interestingly, the paper also discusses the comprehensive additional analyses undertaken by the HAPINZ 3.0 team to check for bias and to confirm the NO2 findings. [ABSTRACT FROM AUTHOR]

Climate change is the most relevant discussion point in today's scenario. The research paper explains and analysis the relationship between crime and air pollution. It is important to understand that there is a remarkable increase in crimes in the recent past globally and the same can be correlated to air pollution. Studies suggest that air pollution contributed to the increased crime rate. Recent studies in this context have suggested that air pollution can be directly related to aggressive behavior and which in turn leads to an increase in the crime rate. The paper focuses on harm mitigation rather than retribution when dealing with environmental offences. Awareness of the application of restorative processes in cases of environmental harm is ambiguous and, indeed, exculpatory in nature in present scenario. It is generally accepted that environmental crimes are among some of the most lucrative types of transnational illegal activity. In 2016, their monetary worth is calculated at between US$ 91 and 259 billion annually, most likely the world's fourth largest criminal sector after narcotics, counterfeits, and trade in human beings. In this area, there is indeed a detailed list of violations, i.e., wildlife crimes, illicit mining are few to mention. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively. [ABSTRACT FROM AUTHOR]

The article presents abstracts on physical anthropology topics which include comparison between the bone density and loading in the knee joint of modern humans and gorillas, indoor air pollution in rural areas of developing countries, and Y chromosome haplotypes and Native American origins.

Polyethylene mulch films used in agriculture are a major source of plastic pollution in soils. Biodegradable plastics have been introduced as alternative to commonly-used polyethylene. Here we studied the interaction of earthworms ( Lumbricus terrestris ) with polyethylene and biodegradable plastic mulches. The objective was to assess whether earthworms would select between different types of mulches when foraging for food, and whether they drag macroscopic plastic mulch into the soil. Laboratory experiments were carried out with earthworms in Petri dishes and mesocosms. The treatments were standard polyethylene mulch, four biodegradable plastic mulches (PLA/PHA [polylactic acid/polyhydroxy alkanoate], Organix, BioAgri, Naturecycle), a biodegradable paper mulch (WeedGuardPlus), and poplar litter, which served as control. Four and three replicates for the Petri dish and mesocosm experiments were used, respectively. Macroscopic plastic and paper mulch pieces (1.5 cm × 1.5 cm and 2 cm × 2 cm) were collected from an agricultural field after a growing season, after being buried in the soil for 6 and 12 months, and after being composted for 2 weeks. We found that earthworms did not ingest polyethylene. Field-weathered biodegradable plastic mulches were not ingested either, however, after soil burial and composting, some biodegradable plastics were eaten and could not be recovered from soil any longer. Earthworms, when foraging for food, dragged plastic mulch, including polyethylene and biodegradable plastic, and poplar leaves into their burrows. The burial of macroscopic plastic mulch underground led to a redistribution of plastics in the soil profile, and likely enhances the degradation of biodegradable mulches in soil, but also can lead to leaching of plastic fragments by macropore flow. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most severe environmental problems that Mexico is currently facing. The objective of this paper is to quantify the most relevant socioeconomic driving forces behind air polluting emissions and, more specifically, 7 local pollutants in Mexico. We do so in a multilevel version of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that accounts for the spatial heterogeneity at the municipal level across the country. The results show that the most relevant variables to determine the emissions of atmospheric pollutants are the population, the harvested area and the number of cars, while technological development helps to mitigate such emissions. The ecological elasticities are, in all cases, smaller than one. Our purpose is to provide quantitative information about these socioeconomic driving forces of air deterioration as a basis to establish some recommendations for environmental policy decision-making. [ABSTRACT FROM AUTHOR]