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Buildings are often located near ambient air pollution sources such as wildfire or heavy traffic areas. While windows in buildings are intermittently open for free cooling or natural ventilation, increased leakage area can lead to elevated human exposure to air pollutants of outdoor origin. The objective of this study is to investigate the effectiveness of paper filter windows in reducing exposure to outdoor air pollution and improving indoor air quality. The physical properties of paper windows as filtration media were experimentally determined, based on which multi-zone indoor air quality and ventilation analysis (CONTAM) simulations were performed for a full-scale building. The results show that the outdoor-indoor air exchange rate of a building can increase about 100% when conventional windows are replaced with paper filter windows. Even with the increased air exchange rate, the infiltration of outdoor particles into the building was reduced about 57–77% for the particle size range of 7–300 nm. These findings imply that paper windows have potential benefits for controlling both outdoor originated pollutants and indoor-generated pollutants with minimal energy inputs, especially in cities and communities impacted by urban air pollution and wildfires. [Display omitted] • We studied removal of outdoor particles due to paper filter windows in buildings. • Paper windows reduced indoor concentrations of outdoor particles up to 77%. • Due to paper windows, air exchange rate of the building increased about 100%. • Paper windows reduced indoor-generated pollutants (e.g., CO 2) by 35–45%. Synopsis: This study investigates filtration and ventilation performances of paper filter windows. [ABSTRACT FROM AUTHOR]

Abstract To reduce pollution emissions and achieve a cleaner production in paper industry, it is essential to understand the inner links between concentrations of gaseous pollutants, types of final products and production techniques. In this study, the concentrations of four potential gaseous pollutants, total volatile organic compounds (TVOC), formaldehyde (HCHO), hydrogen sulfide (H 2 S), and hydrocarbon compounds (C x H y), in the ambient air on thirty sampling points of five pulp and paper mills were detected and analyzed. The results were summarized with respect to the following aspects: (1) Levels of four gaseous pollutants on all the sampling points in five mills. It was obvious that the situation of gaseous pollution from pulp mill was much more serious than those from paper mills. (2) Gaseous pollutant distinctions due to different production processes. The levels of gaseous pollutants from five mills were greatly influenced by the utilized raw materials and the applied paper chemical agents (flavor, coating materials and additives) in mills. (3) Gaseous pollution comparisons at common points. At the two common points in five mills, i.e. the wet end of paper machine and the wastewater treatment process, TVOC and HCHO, C x H y and H 2 S were the main gaseous pollutants, respectively. In addition, some feasible measures to realize a cleaner production in pulping and papermaking process were also put forward in the article. Highlights • We analyze the levels of 4 gaseous pollutants on 30 sites of 5 pulp & paper mills. • Gaseous pollution distinctions due to different production processes are studied. • Feasible measures to realize the cleaner production in the processes are suggested. • Gaseous pollution are greatly affected by raw materials and added paper chemicals. [ABSTRACT FROM AUTHOR]

Abstract: Papermaking enterprises are major polluters that cause water pollution and air pollution. This paper aims to provide analysis of the energy saving and emission reduction in papermaking enterprises.It is the inevitable choice of establishment industrial ecology park as the leading of the papermaking industry by the implementation of cleaner production and circulation economy. Shandong Haiyun efficient ecological park is taken for example.Researches of the developing strategies and policy support of energy saving and emission reduction is discussed. The paper is divided into five parts, the first part is the preface, the second part introduces the general of Haiyun efficient industrial park, the third part puts forward the scheme of clean production of Haiyun efficient ecological industrial park, the fourth part puts forward ecological industry pattern of Haiyun efficient ecological industrial park, the fifth part puts forward policy guarantee measures, and the sixth part makes the summary. [Copyright &y& Elsevier]

Abstract On-site spatial variation study of airborne trace metals has been known to be the key to providing a comprehensive evaluation of air pollution information at any targeted location. However, the existing portable approaches either do not allow sample analysis in the longitudinal direction or is not yet practically applicable due to due to the lack of a portable detection method. In this paper, by integrating paper based colorimetric detection via cellphone and unmanned aerial vehicle (UAV) in-air sampling, we present an approach for on-site multiaxial quantification of airborne trace metals in an arrayed format. Using a self-built sampler mounting on a UAV, our approach enables automatic, multiaxial air PM sampling. In addition, by relying only on a cellphone and a custom-made field reaction kit, samples collected in-air can be readily processed, detected in an arrayed format and interpreted on-site within 30 min. Finally, an ultrafast batch-to-batch paper microfluidic chip fabrication protocol enables 48 chips to be fabricated under 30 s at a cost of 1.92 $, making the approach well-suited for disposable on-site use. Our system was first calibrated for 6 metals commonly found in airborne PM (i.e. Co, Cu, Fe, Mn, Cr and Ni), and the corresponding metals detection limits were found to be 8.16, 45.84, 1.86 × 102, 10.08, 1.52 × 102 and 80.40 ng. The validity of our approach was then demonstrated by characterizing 6 metals commonly found in air PM using a certified ash sample, and the experimentally determined metal weight percentage showed a good agreement with the manufacture certified value. Finally, the approach was used for on-site airborne trace metals spatial variation study at 4 difference locations in Fuzhou City (China), thus demonstrating the significance of our approach in supplementing air pollution information gathering and progressing rapid, on-site air toxicity assessment research. Graphical abstract Image 1 Highlights • Cellphone-based on-site quantification of airborne trace metals in an arrayed format. • High-throughput, automatic colorimetric images process using a self-developed app. • Automated multiaxial ambient pollutants sampling using a modified unmanned aerial vehicle. • Ultrafast batch-to-batch chip fabrication protocol to further reduce the cost per analysis. [ABSTRACT FROM AUTHOR]

The climate change policy debate and ensuing discussions about industrial energy use and carbon emissions have highlighted the need to: (a) aggregate engineering information to a level relevant for economic policy analysis while maintaining sufficient detail so that results are meaningful for industry decision makers, (b) properly represent an industry’s capital vintage structure to better understand inertia associated with changes in aggregate industrial emissions profiles, and (c) identify policy instruments that leverage an industry’s potential for technological change such that carbon emissions can be noticeably reduced. This paper presents an econometric analysis of energy use and emissions profiles of the US Pulp and Paper Industry and uses the resulting set of equations to specify a dynamic model for the analysis of select climate change policies. Scenarios of cost of carbon, energy tax, and investment-led policies indicate that a combination of cost of carbon and investment-led policies can achieve the desired result of rapidly improving overall efficiency of the industry and promoting changes in fuel mix, which together can result in drastic reductions of carbon emissions. [Copyright &y& Elsevier]

Abstract: Burning joss paper, a common practice in temples in some Asian countries, can release toxic pollutants. This study investigated polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F) emissions and profiles from burning joss paper in a temple furnace connected to two wet scrubbers. The mean total PCDD/F content and corresponding toxic equivalent quantity (TEQ) in joss paper were 193 ng kg−1 and 0.645 ng I-TEQ kg−1, respectively, whereas those in bottom ash from burned joss paper were 18.5 ng kg−1 and 1.92 ng I-TEQ kg−1, respectively. The wet scrubbers decreased individual PCDD/F emissions by 26.7–71.0% and those of total PCDD/Fs and I-TEQ by 47.2% and 66.0%, respectively. The total PCDD/F TEQ emission factors before and after the wet scrubbers were 8.14 and 3.42 µg I-TEQ ton-feedstock−1, respectively. The estimated total PCDD/F and corresponding TEQ emissions were 5.29 g year−1 and 0.462 g I-TEQ year−1, respectively, in Taiwan. Burning joss paper in temple furnaces is a significant source of PCDD/F emissions. [Copyright &y& Elsevier]

Abstract: Cooling tower emissions are harmful for several reasons such as air polluting, wetting, icing and solid particle deposition, but mainly due to human health hazards (i.e. Legionella). There are several methods for measuring drift drops. This paper is focussed on the sensitive paper technique, which is suitable in low drift scenarios and real conditions. The lack of an automatic classification method motivated the development of a digital image process algorithm for the Sensitive Paper method. This paper presents a detailed description of this method, in which, drop-like elements are identified by means of the Canny edge detector combined with some morphological operations. Afterwards, the application of a J48 decision tree is proposed as one of the most relevant contributions. This classification method allows us to discern between stains whose origin is a drop and stains whose origin is not a drop. The method is applied to a real case and results are presented in terms of drift and PM10 emissions. This involves the calculation of the main features of the droplet distribution at the cooling tower exit surface in terms of drop size distribution data, cumulative mass distribution curve and characteristic drop diameters. The Log-normal and the Rosin–Rammler distribution functions have been fitted to the experimental data collected in the tests and it can been concluded that the first one is the most suitable for experimental data among the functions tested (whereas the second one is less suitable). Realistic PM10 calculations include the measurement of drift emissions and Total Dissolved Solids as well as the size and number of drops. Results are compared to the method proposed by the U.S. Environmental Protection Agency assessing its overestimation. Drift emissions have found to be 0.0517% of the recirculating water, which is over the Spanish standards limit (0.05%). [Copyright &y& Elsevier]

Abstract: The paper deals with the application of size exclusion chromatography (SEC) for the studies of paper degradation phenomena. The goal is to solve some of the technical problems connected with the calibration of multi-detector SEC system and to find the correlation between SEC and viscometric results of degree of polymerization of cellulose. The results gathered for the paper samples degraded by acidic air pollutant (NO2) are used as an example of SEC–MALLS application. From the correlation between intrinsic viscosities and absolute value of molecular masses obtained with SEC/MALLS (Multi Angle Laser Light Scattering) technique, Mark-Houwink coefficients for cellulose in cupri-ethylenediamine solution were determined. Thus obtained coefficients were used for the determination of viscometric degree of polymerization (molecular mass) of the aged samples. An excellent correlation was found between the chromatographic values of molecular masses obtained with SEC–UV/VIS detection and the viscometric ones utilizing the improved values of Mark-Houwink coefficients. [ABSTRACT FROM AUTHOR]

Atmospheric chemical composition affects foliar chemical composition, which in turn influences the dynamics of both herbivory and decomposition in ecosystems. We assessed the independent and interactive effects of CO2 and O3 fumigation on foliar chemistry of quaking aspen (Populus tremuloides) and paper birch (Betula papyrifera) at a Free-Air CO2 Enrichment (FACE) facility in northern Wisconsin. Leaf samples were collected at five time periods during a single growing season, and analyzed for nitrogen, starch and condensed tannin concentrations, nitrogen resorption efficiencies (NREs), and C:N ratios. Enriched CO2 reduced foliar nitrogen concentrations in aspen and birch; O3 only marginally reduced nitrogen concentrations. NREs were unaffected by pollution treatment in aspen, declined with O3 exposure in birch, and thisdecline was ameliorated by enriched CO2. C:N ratios of abscised leaves increased in response to enriched CO2 in both tree species. O3 did not significantly alter C:N ratios in aspen, although values tended to be higher in +CO2+O3 leaves. For birch, O3 decreased C:N ratios underambient CO2 and increased C:N ratios under elevated CO2. Thus, under the combined pollutants, the C:N ratios of both aspen and birch leaves were elevated above the averaged responses to the individual and independent trace gas treatments. Starch concentrations were largely unresponsive to CO2 and O3treatments in aspen, but increased in response to elevated CO2 in birch. Levels of condensed tannins were negligibly affected by CO2 and O3 treatments in aspen, but increased in response to enriched CO2 in birch. Results from this work suggest that changes in foliar chemical composition elicited by enriched CO2 are likely to impact herbivory and decomposition [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]

Various forms of pollution carry a substantial burden with respect to increasing the risk of causing and exacerbating noncommunicable diseases, especially cardiovascular disease. The first part of this 2-part series on pollution and cardiovascular disease provided an overview of the impact of global warming and air pollution. This second paper provides an overview of the impact of water, soil, noise, and light pollution on the cardiovascular system. This review discusses the biological mechanisms underlying these effects and potential environmental biometrics of exposure. What is clear from both these pollution papers is that significant efforts and redoubled urgency are needed to reduce the sources of pollution in our environment, to incorporate environmental risk factors into medical education, to provide resources for research, and, ultimately, to protect those who are particularly vulnerable and susceptible. [Display omitted] • Increasing evidence suggests that water, soil, noise, and light pollution increase cardiovascular morbidity and mortality. • The mechanisms linking these sources of pollution to cardiovascular disease are diverse, making mitigation efforts challenging, but evolving methods to track these sources of pollution may point to new strategies. • Collectively, these data underscore the urgent need to address pollution in all its forms at a global level. [ABSTRACT FROM AUTHOR]

This paper uses machine learning approaches to predict the association between traffic volume, air pollution, and meteorological conditions. A key focus is on the interaction between these factors. The paper does this using hourly traffic volume, N O x , P M 2.5 , and weather data for Oslo, Norway. I considered a total of six datasets of the 2019 whole-year data to verify the prediction accuracy of the models. I find that the autoregressive integrated moving average model with exogenous input variables, and the autoregressive moving average dynamic linear model outperform the machine learning models in predicting air pollution. At the same time, I also explored the effect of sampling weather subsets on prediction accuracy. Finally, my study makes optimal policy recommendations for reducing air pollution from traffic volume, after considering the interaction and lagged effects of meteorology, time variables, traffic, and air pollution. [ABSTRACT FROM AUTHOR]

The impact of air pollution on public health is substantial, and accurate long-term predictions of air quality are crucial for early warning systems to address this issue. Air quality prediction has drawn significant attention, bridging environmental science, statistics, and computer science. This paper presents a comprehensive review of the current research status and advances in air quality prediction methods. Deep learning, a novel machine learning approach, has demonstrated remarkable proficiency in identifying complex, nonlinear patterns in air quality data, yet its application in air quality prediction is still relatively nascent. This paper also conducts a systematic analysis and summarizes how cutting-edge deep learning models are applied in air quality prediction. Initially, the historical evolution of air quality prediction methods and datasets is presented. This is followed by an examination of conventional air quality prediction techniques. A thorough comparative analysis of progress made with both traditional and deep learning-based prediction methods is provided. This review particularly focuses on three aspects: temporal modeling, spatiotemporal modeling, and attention mechanisms. Finally, emerging trends in the field of air quality prediction are identified and discussed. [ABSTRACT FROM AUTHOR]

Abstract: ; hereafter LHA) (Levin, Z., Halfon, N., Alpert, P., 2010. Reassessment of rain experiments and operations in Israel including synoptic considerations. Atmos. Res. 97, 513–525. DOI:10.1016/j.atmosres.2010.06.011.), reanalyzed the results of the operational seeding in northern Israel between 1975 and 2007 and the preceding Israel 2 cloud seeding experiment (1969–1975) and concluded that there is no net increase in precipitation over the target areas. Our analysis revealed that a synoptic bias during Israel 2 is one of the reasons for the apparent positive effect of seeding in the northern target area and the negative effect in the southern area both of which disappeared in the following experiment in the south (Israel 3; 1975–1995) and the operational seeding in the north. ;hereafter BRG) criticized our paper primarily on the ground that we did not consider the positive results of Israel 1 experiment (1960–1967). It should be noted that in Israel 1 different seeding lines were used from those in both Israel 2 and the operational period. In addition, its raw data is not accessible anymore for reanalysis. Furthermore, Israel 2 had been designed as a confirmatory cross-over experiment to Israel 1 and failed to reproduce its promising results with double ratio (DR) of ~1.00, namely, zero rainfall enhancements. The same DR values were also found in Israel 3 and in the operational seeding. Therefore, because of the differences in the two experiments, the lack of access to the raw data and the disappointing results of the confirmatory experiment, we decided to concentrate our analysis on the more recent seeding activities. The attempt by BRG to explain the reduction of the DR to ~1.00 in the operational seeding period by the suppression due to pollution have been disproved by and also fail to explain the sharp decline of the target/control ratio right at the beginning of the operational seeding period when the lucky draw in this area came to its end (see LHA). [Copyright &y& Elsevier]

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]

This study aimed to explore the published research on the relationship between climate change and skin cancer and the implications for prevention, management and further research. Scoping review. This scoping review following JBI methodology reviewed English articles identified in searches of MEDLINE, Embase, CINAHL, Web of Science and Scopus on 14 April 2023. The screening of articles was completed by two independent reviewers. Data were extracted by a single reviewer and checked by another. A causal pathway diagram was iteratively developed throughout the review and was used to categorise the findings. The search identified 1376 papers, of which 45 were included in the final review. Nine papers reported primary research, and 36 papers were reviews, perspectives, commentaries, editorials, or essays. The papers examined climate change influencing behaviours related to ultraviolet exposure (30 papers), ambient temperature (21 papers) and air pollution (five papers) as possible risk factors; occupational, rural, and contextual factors affecting skin cancer (11 papers); and prevention and access to health care in the context of climate change (seven papers). Most papers were published in journals in subject areas other than health. This review identified ultraviolet radiation, occupation, rising temperature, individual behaviour and air pollution as possible influences on skin cancer rates. Furthermore, it highlights the complexity and uncertainties in the relationship between climate change and skin cancer and the need for further research on this relationship, including primary epidemiological research and reviews that follow recognised review guidelines and include assessment of health services and social determinants in the causal pathways of this relationship. [ABSTRACT FROM AUTHOR]

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms. [ABSTRACT FROM AUTHOR]

This paper focuses on the combustion of hydrogen in boilers, as it appears to be a more effective method than using fuel cells for heating purposes due to higher boiler efficiency. One of the main disadvantages of hydrogen combustion in air is NO x formation. Therefore, the authors decided to introduce corona discharge as an innovative technique to clean hydrogen flue gas by effectively reducing NO x levels. The method involves generating positive plasma at atmospheric pressure by applying up to a 23 kV voltage difference between rod and ring-shaped electrodes. Experimental studies have shown that corona discharge can significantly lower the concentrations of NO and NO x in exhaust gases. The maximum DeNO x level was found to be 32.3%, while the plasma generator uses 17.5% of the power contained in burned hydrogen. The findings suggest that this technology holds potential for application in industrial hydrogen combustion systems, offering an environmentally friendly alternative to conventional NO x reduction methods. [Display omitted] • Corona discharge is promising approach for cleaning hydrogen flue gas from NO x. • The level of NO x removal depends on their concentration. • After plasma treatment, the level of NO decreases while NO 2 increases. • The maximum investigated level of DeNO x was 32.3%. [ABSTRACT FROM AUTHOR]

• Quantifying the impact of peer pressure on environmental improvements among local governments in China. • Investigating cities ' reaction to declining air quality rankings through regression discontinuity design. • Demonstrating the efficacy of political incentives in combating pollution and the catalytic effect of rankings on policy action. • Assessing the benefits resulting from air pollution reductions using an exposure response function. This paper estimates the impact of peer governmental pressure on air quality using a regression discontinuity design inherent in China's air quality ranking system. Because air quality rankings are important for political evaluations, local officials who witnessed a decline in air quality ranking are motivated to enforce more stringent environmental standards compared to their counterparts with increased rankings. By exploiting this regulatory discontinuity with monthly air quality ranking data from 2012 to 2020 of 169 cities, we find that cities experiencing a lower air quality ranking in the current month compared to the previous month exhibit a subsequent improvement in air quality during the following month, as evidenced by decreased pollutant concentrations. Furthermore, our analysis reveals that in cities where pollutant rankings decline more sharply, officials are less likely to encounter fewer promotion opportunities. Importantly, we observe that the discontinuity in individual pollutant concentrations is not solely influenced by their respective rankings but is also impacted by the overall A ir Q uality I ndex (AQI) ranking. Linking the reduction in air pollutants concentration with the economic benefits, a back-of-the-envelope calculation indicates the decrease in premature deaths due to cardiovascular and respiratory diseases amounts to approximately 200 thousand, resulting in an associated economic saving of more than 600 billion Chinese yuan. [ABSTRACT FROM AUTHOR]

The global problem of climate change and difficulties related to energy access, air pollution, and water pollution in India (and other developing countries) necessitate the need to look for alternative clean, local, and low-carbon energy sources for displacing fossil fuels. This paper analyses the existing waste-to-biomethane potential of the sericulture, fisheries, and agro-industrial sectors in India, which have mostly been neglected previously in India's waste-to-energy assessments. In the process, the volume of wastewaters generated from India's agro-industries are also estimated, data on which are scarce in the academic and grey literature. India's annual biomethane potentials (for 2018–19) were found to be 88 million m3, 207 million m3, and 3514 million m3 from the three sectors, respectively. To assess the energy generation potential of these wastes, a comprehensive literature review was conducted and data on residue-to-product ratios, results of proximate analyses, and specific biomethane yields were either compiled or calculated. The gross thermal energy and electricity generation potentials are 132 PJ and 22 TWh respectively; between 19 and 95 million people can benefit from such waste-to-energy production, depending on the end use of the biomethane for electricity generation or as a domestic cooking fuel. The corresponding mean removal of organic pollution from India's surface waters can be between 5.5 and 9.3 million tonnes of chemical oxygen demand. For the end uses of biomethane in electricity generation and cooking, the cumulative GHG abatements attributable to these industries are 11.4 million tonnes and 6.8 million tonnes of carbon dioxide-equivalent respectively. Besides the numeric results reported, this paper also contains a comprehensive compilation of data on waste characteristics and biomethane productions. The present status of biogas generation and related policy implementation in India are reviewed and discussed. Overall, our findings indicate that biomethane from these wastes and wastewaters can generate substantial energy for India, is in line with recent government policies, and can aid India in achieving six Sustainable Development Goals of the United Nations. • Annual estimates of wastes from India's sericulture, fisheries and agro-industries • The corresponding annual biomethane generation potential is 3810 million m3. • Clean electricity or cooking gas for 19 and 95 million people respectively • Quantitative estimates of atmospheric GHG and surface water COD removals • Biomethane data compilation and a review of present status and policies in India [ABSTRACT FROM AUTHOR]

The inconvenience of travel, air pollution and consequent economic losses caused by traffic congestion have seriously restricted the healthy and sustainable development of cities in China. In this context, as the main component of current and future urban traffic management measures, intelligent transportation system is an important means to improve the traffic efficiency of road network and alleviate urban traffic congestion. Traffic flow prediction plays an important role in this connection. In this paper, an ensemble short-term traffic flow prediction method based on optimized variational mode decomposition (OVMD) and combined long short-term memory network (LSTM) is proposed. The method consists of three main components: 1. Use the improved bat algorithm to optimize the parameters of VMD to achieve better decomposition effect; 2. Use the optimized variational mode decomposition algorithm (OVMD) to decompose the unstable original traffic flow time series data into relatively stable multiple Intrinsic Mode Functions (IMFs); 3. The optimized L-BILSTM model is established by combining the basic long short-term memory network with the bidirectional long short-term memory network. It can better extract information from traffic flow data and improve the accuracy of prediction results. In the empirical study part, the traffic flow data of Changsha City is used to verify the prediction model proposed in this paper. The influence of the application of the variational mode decomposition algorithm to the training set data and the overall data on the final prediction results is also compared and analysed. [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]

Governments have implemented many regulations to solve traffic congestion and pollutant emissions. Traffic restriction (TR) policy is the most common and widely used traffic regulation. However, it is difficult to alleviate traffic pollution through TR policies. Only a few studies focus on TR policies in multiple cities. In this paper, we classify traffic restrictions by geographical factors and evaluate the impact of different policies based on PSM-DID first. Then, we classify the mixed restrictions and test their effects on air pollution. The results show that TR policy improves air quality regardless of the season. Moreover, the effect of TR policy is not affected by the superposition of the type of traffic restriction. For cities implementing the same TR policy, geographic factors can lead to different outcomes. This finding provides a reference for governments in reducing air pollution. • Traffic restrictions significantly affected air quality in different regions of China. • Cities implemented traffic restrictions have improved air quality in adjacent cities. • Effect of traffic restrictions does not depend on the number of mixed restrictions. [ABSTRACT FROM AUTHOR]

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors. [ABSTRACT FROM AUTHOR]

While combating air pollution may seem to be a corporate responsibility, what individuals contribute can be substantial. This study explores the individual's behavioral intention to reduce the air pollution associated with vehicles, household appliances, and religious practices. An extended theory of planned behavior (TPB), past behavior included, serves as the analytical model, while the approach of partial least squares structural equation modeling (PLS-SEM) is employed in the model's estimation. The results show that subjective norms represent the primary driver of individual intentions to reduce air pollution, followed by perceived behavioral control, past behavior, and attitude, in that order. Surprisingly, advocacy on the part of the government to reduce the burning of incense and joss paper, disregarding years of social conflicts and protests, plays the most important role among all indicators within the construct subjective norms. This result holds even within the subsample of believers in religions that involve the practice of burning incense and joss paper. • Subjective norms represent the primary driver of individual intentions to reduce air pollution. • Governmental advocacy to reduce the burning of incense and joss paper plays an important role. • Individuals' attitudes towards de-carbonization and mitigating air pollution differ. [ABSTRACT FROM AUTHOR]

This paper examines the impact of policy uncertainty on air pollution, using the turnover of local officials in China as a quasi-natural experiment. The results from the two-way fixed effects model show that policy uncertainty caused by the turnover of different officials produces contrasting effects on air pollution. While the turnover of the mayor has no effect on air pollution, the turnover of the municipal party committee secretary deteriorates it. Two-Stage Least Square estimation confirms these results where the age and tenure of officials were used as instrumental variables of policy uncertainty. The results are robust after endogenous treatments and robustness tests. The mechanism analysis further indicates that policy uncertainty worsens air quality by weakening environmental regulation and crowding out green innovation. Moreover, the impact is more pronounced in smaller and less developed cities. The conclusions provide policy implications for environmental governance in the period of policy uncertainty. [ABSTRACT FROM AUTHOR]

• This empirical study investigated WFH's impact on air quality and mechanisms. • WFH reduced the intra-city travel intensity (ITI) by 65.0 % and the AQI by 26.3 %. • ITI completely mediated WFH's impact on air pollutants NO 2 and PM 10 emissions. • WFH has the potential to become a sustainable urban development strategy. Working from home (WFH) arrangements are already acknowledged as a sustainable measure for alleviating urban congestion and air quality issues, but fewer large-scale empirical studies investigating the benefits and mechanisms of WFH on air pollution (AP). The exogenous shock of the epidemic provides unique conditions that enable us to conduct empirical studies on the positive impacts of working from home. Building upon this background, this paper designs a series of empirical models to investigate the benefits of WFH on AP and the mediating effect of intra-city travel intensity (ITI) concerning the WFH-AP relationship to depict the mechanism of the impact of WFH on AP. This paper collected panel data on ITI and AP from January 1 to March 15, 2020, in 19 cities in China for regression analyses. The findings indicate that WFH reduces ITI by 65.0 %, while an increase in ITI leads to higher AP emissions, particularly of NO 2. WFH positively impacts urban air quality, resulting in a 26.3 % decrease in the Air Quality Index (AQI) and reductions of 17.2 %, 4.1 %, 32.7 %, 18.8 %, and 13.6 % for the five air pollution categories (CO, NO 2 , PM 2.5 , PM 10 , SO 2) respectively. Intermediate analyses demonstrated that ITI partially mediated the decrease in AQI, PM 2.5 , and CO, while PM 10 and NO 2 showed complete mediation. However, ITI had a weak mediating effect on SO 2 emissions. The contribution of this study is that it provides supporting evidence that working from home positively impacts urban air quality and commuting. Additionally, it offers urban administrators a fact-based approach to traffic management and environmental protection, pointing to important directions for future urban sustainability strategy development. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has reinforced an interest in the relationship between air pollution and respiratory viral infections, indicating that their burden can be increased under poor air quality. This paper reviews the pathways through which air pollutants can enhance susceptibility to such infections and aggravate their clinical course and outcome. It also summarizes the research exploring the links between various viral infections and exposure to solid and gaseous pollution in Poland, a region characterized by poor air quality, especially during a heating season. The majority of studies focused on concentrations of particulate matter (PM; 86.7%); the other pollutants, i.e., BaP, benzene, CO, NO x , O 3 , and SO 2, were studied less often and sometimes only in the context of a particular infection type. Most research concerned COVID-19, showing that elevated levels of PM and NO 2 correlated with higher morbidity and mortality, while increased PM 2.5 and benzo[a]pyrene levels were related to worse clinical course and outcome in hospitalized, regardless of age and dominant SARS-CoV-2 variant. PM 10 and PM 2.5 levels were also associated with the incidence of influenza-like illness and, along with NO 2 concentrations, with a higher rate of children's hospitalizations due to lower respiratory tract RSV infections. Higher levels of air pollutants also increased hospitalization due to bronchitis (PM, NO x , and O 3) and emergency department admission due to viral croup (PM 10 , PM 2.5 , NO x , CO, and benzene). Although the conducted studies imply only correlations and have other limitations, as discussed in the present paper, it appears that improving air quality through reducing combustion processes in energy production in Poland should be perceived as a part of multilayered protection measures against respiratory viral infections, decreasing the healthcare costs of COVID-19, lower tract RSV infections, influenza, and other respiratory viral diseases prevalent between autumn and early spring, in addition to other health and climate benefits. [Display omitted] • Polluted air may increase the severity of respiratory viral infections. • Poland is a PM and BaP pollution hot spot in Europe. • Air pollution is associated with a higher burden of respiratory viral infections. • Improving the air quality should be a protection measure against infections. • Further studies are encouraged to explore the relationships with other pathogens. [ABSTRACT FROM AUTHOR]

Due to the positive externalities of public transit, fares have been heavily subsidized throughout the United States and the world. Policymakers have designed these subsidies as a potential mechanism to reduce local air pollution. This paper evaluates the effectiveness of free-fare public transit subsidies in reducing air pollution. Studying the "Zero Fare for Better Air" initiative, I find no significant decrease in ground-level ozone due to free-fare public transit in Colorado. This paper also demonstrates how the free-fare initiative failed to improve welfare even when incorporating all the positive externalities of transit ridership. [ABSTRACT FROM AUTHOR]

In this study, we investigate whether unpleasant mood caused by air pollution leads to lower analyst forecast optimism. Using a sample of 103,768 observations from 2014 to 2021, we show that there is a negative association between air pollution and analyst forecast optimism. Furthermore, we find that analysts become more optimistic forecasters after moving to a city with lower level of air pollution. Our results hold through various robustness tests including alternative measures of air pollution, controlling for various fixed effects, 2SLS by instrumenting for air pollution using thermal inversions and a placebo test by randomizing the headquarter locations of brokerage firms in our sample. Our paper provides plausibly causal evidence that air pollution lowers analyst forecast optimism. • This paper investigates whether unpleasant mood caused by air pollution leads to lower analyst forecast optimism. • There is a negative association between air pollution and analyst forecast optimism. • Analysts become more optimistic forecasters after moving to a city with lower level of air pollution. • The results hold through various robustness tests. [ABSTRACT FROM AUTHOR]

Abstract: This study examines whether converting the fossil fuel of the Tel Aviv power station from oil to gas influences air pollution in the local urban environment. To this end, the spectral properties of accumulated dust on tree leaves and paper bags were assessed before (2004) and after (2006) the conversion. The sampling site was a garden in a neighborhood located 2700m downwind of the power station. In addition, air pollution concentrations and particulate matter parameters recorded by a local meteorological station were analyzed (PM10, NO x , NO2, NO, and SO2). Although differences in the average monthly concentration of pollution parameters are mostly insignificant between the two periods, the accumulated particulate matter exhibits considerably different spectral patterns. All first period samples exhibit a distinctly concave slope in the spectral region between 400 and 1400nm, indicative of high amounts of soot, most likely due to the combustion products of fuel oil exhausted by the power plant. In contrast, the second period samples exhibit spectra that indicate reduced soot content and even appear slightly convex, evidencing the presence of dust of mineral origin, a feature likely masked by the soot in the first period. Thus, the spectral data support that the power plant conversion results in less pollution. More generally, this study corroborates that VIS-NIR-SWIR spectroscopy characterizes key properties of the particulate layer accumulating on sampled surfaces and thus, is a powerful method for monitoring the urban environment. [Copyright &y& Elsevier]

Given the idea that financial institutions can have a significant impact on environmental performance, green credit, which essentially involves capital allocation under environmental awareness, is worth to be explored and implemented by banking sectors worldwide. This paper explores the impact of environmental awareness on firms' loan rates, with a focus on borrowers who have a record of environmental penalties. Using a comprehensive loan dataset from one of China's "Big Five" banks between 2010 and 2013, we employ the volatility of particulate matter 2.5 (PM 2.5) as a proxy to measure environmental awareness. Our findings suggest that the volatility of air pollution has a positive effect on the floating ratio of the loan interest rate for firms with a history of environmental penalties. Furthermore, we find that this mechanism is particularly pronounced for private-owned enterprises (POEs), new loan customers, firms with low crediting ratings, and small-sized enterprises. We also confirm the role of environmental awareness in firms' loan costs by demonstrating that the volatility of air pollution is sensitive to variations in insolation duration, environmental attention, and PM2.5 monitoring stations. We suggest that the volatility of air pollution is a more meaningful indicator than the degree of air pollution, and that people's voluntary environmental awareness can transfer the environmental risk of firms to their internal operating costs. The significance of this study lies in its potential to aid policymakers and investors in advancing sustainable finance practices, as well as stimulating individual engagement with environmental awareness and prompting financial institutions to give greater importance to these issues. • Environmental awareness increases non-compliant firms' loan costs. • Environmental awareness can transfer the environmental risk of firms to their operating costs. • The mechanism more strongly impacts POEs and small-size firms. • Sensitivity of environmental awareness to insolation, attention, and PM 2.5 monitoring. • The volatility of air pollution is a more meaningful predictor than the degree of air pollution. [ABSTRACT FROM AUTHOR]

• The relationship between industrial agglomeration and air pollution is explored. • The mediating effect of technological innovation on air pollution is explored. • The dynamic spatial panel Durbin model and mediation effect model is used. Based on the panel data of 277 prefecture-level cities in China, this paper empirically analyzes the relationship between industrial agglomeration and air pollution, and the mediating effect of technological innovation by using dynamic spatial panel Durbin model and mediation effect model. The results show that: (1) there is a typical N-shape curve relationship between industrial agglomeration and air pollution, that is, there is a moderate agglomeration interval in which industrial agglomeration can restrain air pollution. (2) Technological innovation is a mediator variable of industrial agglomeration affecting air pollution. Industrial agglomeration not only has a direct impact on air pollution, but also has an indirect impact on air pollution through technological innovation. The non-linear mediating effect is reflected in the typical U-shape curve relationship between technological innovation and air pollution. (3) Air pollution and technological innovation have strong time-space dependent effects. [ABSTRACT FROM AUTHOR]

The high-speed rail (HSR) network in China has experienced rapid development since the 2000s. In 2016, the State Council of the People's Republic of China issued a revised version of the "Mid- and Long-term Railway Network Plan", detailing the expansion of the railway network and construction of an HSR system. In the future, the HSR construction efforts in China will further increase, which is considered to impact regional development and air pollutant emissions. Therefore, in this paper, we apply a transportation network-multiregional computable general equilibrium (CGE) model to estimate the dynamic effects of HSR projects on economic growth, regional disparities, and air pollutant emissions in China. The results indicate that HSR system improvement could generate a positive economic impact but could also increase emissions. The gross domestic product (GDP) growth per unit investment cost stimulated by HSR investment is found to be the largest in eastern China but the smallest in the northwest regions. Conversely, HSR investment in Northwest China contributes to a substantial reduction in regional disparities in terms of the GDP per capita. In regard to air pollution emissions, HSR construction in South-Central China results in the largest increase in CO 2 and NO X emissions, while for CO, SO 2 , and fine particulate matter (PM 2.5) emissions, the largest increase occurs due to HSR construction in Northwest China. At the regional level, the provinces with large changes in accessibility also experience large changes in their air pollutant emissions. [ABSTRACT FROM AUTHOR]

This paper, based on the panel data of 284 Chinese prefecture level and above cities span 2010 to 2019, systematically investigated the relationship between digital economy and air pollution, with introducing difference-in-differences model, mediating effect model, and spatial difference-in-differences model, as well as treating the "National Big Data Comprehensive Pilot Area" policy as a quasi natural experiment. Finally, results show that:(1) Digital economy does negatively affect air pollution, in particular the industrial dust, followed by industrial sulfur dioxide and carbon dioxide. All these benchmark results have withstood the robustness test. (2) Almost all the mediating roles of industry structure upgrading, industry intensive development, and online life in the relationship between digital economy and air pollution have been confirmed, except it is partly absent in the digital economy-industrial dust nexus and the digital economy-industrial sulfur dioxide linkage. (3) Negative spatial spillover effect is evident between digital economy and air pollution, regardless of involving the neighboring pilot cities or the non pilot cities. (4) The passive air pollution effect of digital economy varies with the regional distribution, administrative scale, marketization level, and government competitiveness. All these findings shed new lights on government intervention. [ABSTRACT FROM AUTHOR]

Based on the panel data of various daily air pollution indicators of 288 prefecture-level cities in China from January 1, 2015 to December 31,2018, this paper takes the first round of central environmental protection inspection (CEPI) as a quasi-natural experiment, and empirically analyzes its impact on urban air pollution by using multi-period difference-in-difference (DID) method. The results show that: (1) the first round of five batches of environmental inspection and two batches of "looking back" have significantly reduced the concentration of seven pollutants; (2) The first five batches of environmental inspection have a stronger impact on reducing air pollution than "looking back"; Moreover, in the short tel·in after the inspection, it still has a significant reduction effect on air pollution; (3) The impact of central environmental protection inspection on air pollution shows strong heterogeneity in different batches of inspections, urban scale and economic development level, as well as the air quality level of cities before the inspection. The research results of this paper provide corresponding policy recommendations and decision-making basis for the Chinese central government to further deepen the reform of environmental protection supervision mechanism and promote the emissions of multiple pollutants mitigation. [ABSTRACT FROM AUTHOR]

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

Docked vessels in ports can be one of the dirtiest emitters in terms of local air pollutants. This paper evaluates whether the regulation of Emission Control Areas in China reduces air pollutants emissions in port cities. The transboundary spillovers of air pollution, induced by meteorological conditions, results in air pollution concentrations at specific locations encompassing both locally generated pollutants and those originating from elsewhere. Therefore, it is challenging to discern the emission reduction effects of environmental regulatory policies through air pollutant monitoring concentrations. This paper develops an approach to isolate transboundary spillovers induced by the wind, estimate locally produced pollution, and utilize the concentrations of locally produced pollution to identify the treatment effects of the Chinese Emission Control Areas policy. The results find that the policy has significantly reduced the locally produced sulfur dioxide concentrations by 3.41% in port cities. However, the omission of transboundary spillovers could yield contrasting results, suggesting that ignoring the pollution transport could lead to erroneous conclusions. The research findings of this study hold significant policy implications, highlighting the importance of accounting for transboundary transport of air pollution in evaluating the local government's environmental efforts and implementing supra-city environmental policies to prevent air pollution. • We investigate the efficacy of marine fuel restrictions policy. • We develop a new approach to account for the transboundary air pollution spillovers. • The policy significantly reduced local sulfur dioxide concentrations in ports. • Ignoring pollutant spillovers may bias the estimate of environmental policy effects. [ABSTRACT FROM AUTHOR]

• This paper investigates the impact of air pollution on excess executives' perks. • To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' perk consumption due to social distancing and lockdown policies. • We find that air pollution positively affects excess perks. • External monitors and managerial monetary compensation can curb excess perk consumption. This paper investigates the impact of air pollution on excess executives' perks. To establish causality, we exploit an exogenous event (i.e., COVID-19), which restrained executives' entertainment activities and travelling due to social distancing and lockdown policies. Using a difference-in-difference model and a sample of listed firms in China from 2016 to 2022, we find that excess perks of companies located in polluted cities reduce more due to COVID-19 than those of companies in cities with good air, indicating that air pollution positively affects excess perks. Further analyses demonstrate that external monitors and managerial monetary compensation can curb excess perk consumption. [ABSTRACT FROM AUTHOR]

In recognition of the impacts of climate change and air pollution, a special issue was prepared to address the response and adaptation of agricultural ecosystems to global changes. A total of thirteen papers were published, delving into the mechanisms underlying the effects on crops and agroecosystems and adding important insights into the modeling and adaptation of agroecosystems to global change through optimized management practices. This collection of papers has a broad relevance to numerous areas in agriculture, ecology, and environmental and earth sciences. • A special issue dedicated to 'agricultural ecosystems response and adaptation to global changes'. • A total of thirteen papers were published. • Studies concerned ozone, precipitation, warming, management, and modelling. • This Editorial discusses the content of the publications included in the special issue. [ABSTRACT FROM AUTHOR]

Air pollution and labor mobility are two important social phenomena associated with China's pursuit of high-quality development. First, this paper matches China's urban characteristics data with China Migrants Dynamic Survey in 2017. Then, the conditional logit model is used to empirically investigate the effects and heterogeneity of air pollution on employment location choice. Finally, combined with the concept of risk perception in psychology, this paper explores the underlying mechanisms. The main results are as follows: (1) Air pollution has a significant negative effect on employment location choice of labor force. Specifically, a 1 μg/m3 increase in PM 2.5 concentration in a city can result in a significant decrease of 0.917 percentage points in the probability that the labor force will choose to work in that city. (2) There are heterogeneous characteristics of the negative impact of air pollution on employment location choice. Those with higher education level, outdoor work, relatively higher individual income, and 1–2 minor children are more sensitive to air pollution when choosing employment sites. (3) The mechanism analysis shows that perceived air pollution is an important channel influencing labor's employment location choice, and the stronger the stimulus of air pollution, the greater the negative impact of perceived air pollution on labor force. • This paper matches China's urban characteristics data with China Migrants Dynamic Survey. • The effects and heterogeneity of air pollution on employment location choice is explored. • Combined with the concept of risk perception in psychology, this paper explores the underlying mechanisms. [ABSTRACT FROM AUTHOR]

To adapt to the reality of limited computing resources of various terminal devices in industrial applications, a randomized neural network called stochastic configuration network (SCN), which can conduct effective training without GPU, was proposed. SCN uses a supervisory random mechanism to assign its input weights and hidden biases, which makes it more stable than other randomized algorithms but also leads to time-consuming model training. To alleviate this problem, we propose a novel bidirectional SCN algorithm (BSCN) in this paper, which divides the way of adding hidden nodes into two modes: forward learning and backward learning. In the forward learning mode, BSCN still uses the supervisory mechanism to configure the parameters of the newly added nodes, which is the same as SCN. In the backward learning mode, BSCN calculates the parameters at one time based on the residual error feedback of the current model. The two learning modes are performed iteratively until the prediction error of the model reaches an acceptable level or the number of hidden nodes reaches its maximum value. This semi-random learning mechanism greatly speeds up the training efficiency of the BSCN model and significantly improves the quality of the hidden nodes. Extensive experiments on ten benchmark regression problems, two real-life air pollution prediction problems, and a classical image processing problem show that BSCN can achieve faster training speed, higher stability, and better generalization ability than SCN. • A novel bidirectional stochastic configuration network (BSCN) was proposed to solve regression problems in this paper. • BSCN can greatly accelerate the training efficiency of the SCN model and make the model better in generalization ability and more compact in network structure. • The effectiveness of BSCN has been verified on extensive experiments. • BSCN provides a stable and fast modeling solution for platforms with limited computing ability. [ABSTRACT FROM AUTHOR]

• Waste emissions scenarios are modelled with the rapid assessment software CCAC SWEET. • HIA for PM 2.5 and application of publicly available tool WHO AirQ+. • 94% reduction of long-term mortality due to reductions in PM 2.5 emissions in 2030. • A detailed analysis of the current situation provides suggestions for waste management policies. Despite the clear link between air pollution and health, research to investigate the relationship between municipal solid waste management and air pollution and health has not been prioritized. Such research may generate scientific information that would help reduce population exposure to air pollutants. This paper examines the case study of Accra in Ghana, a city dealing with serious waste management problems. The paper proposes a methodology to estimate the impact of waste management on urban air pollution and health. The analysis is described in the following four steps: (1) collecting data on the waste sector; (2) modeling the emissions arising from waste management; (3) transforming emissions to concentration values and (4) estimating the burdens on health. The assessment has been conducted using the CCAC SWEET tool and WHO AirQ+. The method presented can be used in different locations, depending on data availability, when analyzing the impact of and potential changes to waste sector policies. The results of this health impact assessment indicate that, based on the emissions of PM 2.5 from the waste sector in Accra, a change from the business-as-usual to more sustainable options would reduce air pollutants emissions and avert 120 premature deaths in 2030. Levels of air pollution in Accra are significant and interventions to reduce PM 2.5 exposure should be promoted. The detailed analysis of the current situation provides suggestions for waste management policies in terms of impacts on health and ideas to reconsider the waste policies in Accra. [ABSTRACT FROM AUTHOR]

Increasing expansion of China's coal mine production leads to the increased storage pressure in current coal silos. Most of these coals are stored in open-air coal pile. Therefore, those coal piles have caused a series of environmental pollution problems, which includes endangering workers' health and aggravating air pollution in the surrounding natural environment. Therefore, it is urgent to study and prevent the dust flying problem of coal pile. This paper, based on a single chemical dust suppressant, optimizes a new type of composite binder. This composite binder has advantages of dust suppression effect, relatively simple preparation process and the environmentally friendly agent. This paper is mainly divided into two parts: the study on the influencing factors of dust wetting and the development and application of related spraying systems. Through the energy spectrum analysis of scanning electron microscope and reverse osmosis method, it explores the dust wetting and suppression characteristics of the composite binder with different physical and chemical properties. The optimal application range of the composite binder are obtained. Finally, an automatic spraying system for dust suppression is designed. The composite binder developed in this paper is applied in the open-air coal yard and the results show that the composite binder studied in this paper has a good effect of dust suppression. [ABSTRACT FROM AUTHOR]

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]