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Microplastics (< 5 mm) have lately been identified in the atmosphere of urban, suburban, and even distant places far from plastic particle areas, suggesting the possibility of long-distance atmospheric transport of microplastics. However, the occurrence, fate, transmission, and effects of these suspended atmospheric microplastics (SAMPs) are all currently unknown in the Philippines. This study investigated the presence of suspected microplastic in the atmosphere of sixteen cities and one municipality of Metro Manila, Philippines. Sampling was conducted using a respirable dust sampler mounted with a Whatman GF/C filter paper at an intake flow rate of 1.4 L/min with Whatman GF/C filter paper. Results reveal that all seventeen sampling areas have the presence of SAMPs. A total of 155 SAMPs were found and confirmed in Metro Manila, with the highest concentration in Muntinlupa City and Mandaluyong City (0.023 SAMP/NCM). Fourteen SAMP types were identified across the sampling areas, ⁓ 74% with polyester. This study is the first record of the presence of microplastics suspended in the ambient air in the Philippines. It is estimated that an adult person in Metro Manila has the potential to inhale (5–8 per minute, normal minute ventilation) about 1 SAMP if exposed for about 99.0 to 132 h. Further studies should be done to evaluate the fate and health effects of these SAMPs in Metro Manila's setting. [ABSTRACT FROM AUTHOR]

Industrial pollution in Bangladesh has posed a serious threat to human health, economic activity, and the environment. By emphasizing industries that produce major pollutants, substantial improvements can be made to pollution mitigation measures. In countries where primary pollution data is not readily available, the Industrial Pollution Projection System (IPPS) could be used to calculate the pollution load utilizing total industrial output or employment data. IPPS data, which was designed for developed countries like the USA, had been used directly for other countries without any normalization in previously reported studies. The main purpose of this study is to modify the current IPPS approach for any other country by incorporating specific correction factor for a specific country. In this study, a specific correction factor for Bangladesh was determined, taking into account the country's major polluting industries, and used to estimate the pollution scenario for the year 2020. The accuracy of the specific pollution intensities was also evaluated by comparing the data obtained using both gross output and employee number. According to this study, the top three air-polluting industries are structural clay products, cement-lime-plaster industry, and iron and steel industry. Similarly, for water pollution, the food industry, paper and paper product industry, and textile industry are the largest pollutant contributors. The detailed pollution load matrix in terms of air and water pollution is also developed, and can be used to predict both short-term and long-term scenarios of industrial pollution in Bangladesh, which eventually will assist the policy makers to adopt appropriate pollution management approach. Moreover, the methods developed in this study will help to tailor the IPPS data for any country and increase the accuracy of the pollution load. [ABSTRACT FROM AUTHOR]

The deposition of trace elements around a pulp and paper industry in Morelia, Mexico, was evaluated using two lichen species as biomonitors. Samples of the foliose lichen Flavopunctelia praesignis and the fruticose lichen Usnea ceratina were collected in two remote areas and transplanted at different distances and directions from the pollution source. Lichen samples were exposed for 4 months (1) around the industrial area and (2) in their native habitats (control sites). We investigated the bioaccumulation of 11 trace elements in lichen thalli, and we compared the response of the two lichen species. To identify possible common sources, we evaluated the relationships between trace elements by correlations and cluster analyses. Our results showed that Cd was a good tracer for air pollution from the pulp and paper mills. In samples of Usnea ceratina exposed around the industrial area, Cd was significantly higher than in the remote area. Within the study area, trace element contents increase with the distance from the source, and they showed high depositions in the direction of prevailing winds. Moreover, we were able to detect groups of elements with similar behavior and common origins. Our results indicated that Flavopunctelia praesignis showed a higher capacity to accumulate trace elements than Usnea ceratina. [ABSTRACT FROM AUTHOR]

This study introduces an applicable colorimetric sensor array for the detection of pesticides in the vapor phase. The array consisted of six metal nanoparticles spotted on the piece of filter paper. 3D-origami pattern was used for the fabrication of a paper-based sensor to decrease the effect of the nanoparticles leaching after exposure to analytes. Exposure to pesticide aerosols caused changes in the color of the array due to the aggregation of nanoparticles. These changes provided selective responses to thion pesticides such as malathion, parathion, chlorpyrifos, and diazinon. The sensing assay could also differentiate between aliphatic and aromatic thions and discriminate amine-containing compounds from the other studied analytes. These finding results are clearly confirmed by both visual detection and multivariate statistical methods. The proposed sensor was successfully developed for the quantitative measurement of pesticide aerosols at a very low concentration. The limit of detection of this method determined for malathion, parathion, chlorpyrifos and diazinon were 58.0, 103.0, 81.0 and 117.0, respectively. Moreover, the array could be employed to simultaneously analyze four studied pesticides. The statistcal results confirmed that the method has high performance for concurrent detection of thions as a major air pollutant without the interference of other species. [ABSTRACT FROM AUTHOR]

At present, air pollution remains a serious environmental issue with extensive attention in China. It may not only cause population outflow but also poses significant threats to residents' health. Therefore, it has become an imperative initiative to explore the impact of air pollution on the residents' health. In this paper, we map the spatial distribution of air pollution, population migration, and residents' health between 2010 and 2020 based on panel data from 31 Chinese provinces. Theoretical analysis and empirical tests are then carried out to investigate the intrinsic logical relationships between the three. The research findings demonstrate the following: (1) Air pollution not only has a direct negative impact on residents' health, but it also has an indirect impact on residents' health through the mediating effect of population migration. (2) Air pollution has a significant negative spatial spillover effect on residents' health, and neighboring residents are at greater health risk of being exposed to air pollution than local residents. (3) Economic development, environmental regulation, and their interaction term exhibit a single threshold effect on the health risk of air pollution. (4) From the perspective of regional heterogeneity, we find that the health risk of air pollution is greater in northern China and the southeast of the Hu Line. This paper provides valuable insights for promoting the implementation of the grand strategies of "Beautiful China" and "Healthy China." [ABSTRACT FROM AUTHOR]

Air pollution poses a great danger to human health and economic development, and understanding people's willingness to pay for improved air quality (WTPA) impacts environmental protection. This paper investigates WTPA based on the perspective of subjective well-being (SWB) and analyzes the mediating role of social interaction on the relationship between the two. This paper distinguishes social interactions into online and offline interactions and analyzes whether the mediating effect of the two different interactions on SWB and WTPA exists separately. Using data from the 2018 China General Social Survey (CGSS), we find that SWB has a significant positive effect on WTPA, individuals with higher well-being have higher pro-environmental willingness; there is no age, education level, sex of the person, or regional heterogeneity in the effect of SWB on WTPA; offline social interactions play a partially mediating role between SWB and WTPA, while online social interactions failed to mediate between the two. This paper's policy implication is that improving residents' subjective well-being is both an important development goal and an essential way to resolve the conflict between economic development and environmental protection. [ABSTRACT FROM AUTHOR]

Air quality has a significant impact on human health, in order to alleviate the air pollution and improve the ability to predict the air quality. In this paper, a prediction model of air quality index composed of variational mode decomposition and temporal convolutional network was proposed. First, in order to reduce the non-stationarity and randomness of the time series, the original air quality index sequence was decomposed by variational mode decomposition, and the decomposition number was determined by the central frequency method to decompose into multiple relatively stable sub-sequences with different frequency scales. Then, the decomposed sub-stable sequence was predicted by the time convolutional network. Finally, the prediction data were integrated and reconstructed to obtain the final prediction results. Comparing the results of other forecasting models by performance evaluation metrics, the combined forecasting model proposed in this paper reduced RMSE by 20.9%, 19.2%, 5.1%, 29.9%, 23.7% on the Beijing dataset. MAPE reduced by 26.6%, 22.3%, 19.5%, 28.9%, 15.0%, respectively. MAE decreased by 19.1%, 20.6%, 9.6%, 29.5%, 23.5%. R2 increased by 4.6%, 4.0%, 0.8%, 14.9%, 5.5% respectively. This proves the accuracy and reliability of the proposed model. [ABSTRACT FROM AUTHOR]

Air pollution is now one of the world's most serious environmental problems. It represents a significant hazard to both health and the climate. Urban air quality is steadily declining, affecting not only the air itself but also impacting the quality of water and land. This paper explores the utilization of machine learning-based algorithms for analysis and prediction of air quality in smart cities. In this paper, smart cities for which air quality index (AQI) is calculated are Ahmedabad, Delhi, Lucknow, Gurugram, and Mumbai. The comparative analysis of different Machine Learning algorithms such as Random Forest Regression (RF), Decision Tree Regression, Linear regression, XgBoost and proposed hybrid model which is combination of Random forest and Xgboost model, have been discussed in the paper. The analysis has been carried out using a machine learning-based algorithm to determine which pollutant is the primary source of pollution in a smart city so that preventative steps can be implemented to reduce air pollution. [ABSTRACT FROM AUTHOR]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

Mitigation actions needed to achieve the ambitions of the Paris agreement to limit global temperature rise to 1.5 °C or below 2 °C have to align with sustainable development. In the near term, this implies a better understanding of context-specific challenges in integrating sustainability with climate policies during the designing, planning, implementation and financing stages. Through a review of selected studies across regions, this paper draws out conclusions focussing on mitigation–sustainable development goal (SDG) trade-offs, with implications on costs and equity for different development contexts. Studies show that trade-offs depend on how the option is implemented and at what scale; mitigation options such as afforestation, biomass production and digitalisation are examples of this. Some options could also result in significant adverse environmental impacts as in the case of battery waste and raw material resources for electric vehicle (EV) or air pollution associated with compact urban development. We find the most important factors influencing equity include unequal access (e.g. urban green spaces and public transportation), high costs (e.g. EV) and financial constraints. Major knowledge gaps include (i) limited empirical evidence of SDG-related trade-offs associated with scaling up mitigation options, (ii) limited understanding of the extent to which benefits are experienced by different groups, (iii) an understanding of the extent to which local context was considered when assessing mitigation–SDG interaction, including the engagement of stakeholders and (iv) synergies and trade-offs associated with cross-sectoral policies. The paper recommends ex-post analysis of detailed and place-based cases that document how synergies and trade-offs emerged and how these were addressed. [ABSTRACT FROM AUTHOR]

Affected by the epidemic and other factors, the global economy is in a downturn, and countries around the world are under unprecedented debt pressure. How will this affect environmental protection? Taking China as an example, this paper empirically studies the impact of changes in local government behavior on urban air quality under fiscal pressure. This paper uses the generalized method of moments (GMM) to find that fiscal pressure has significantly reduced PM2.5 emissions, with a unit increase in fiscal pressure will increase PM2.5 by about 2%. The mechanism verification shows that three channels affect PM2.5 emissions: (1) fiscal pressure has prompted local governments to relax the supervision of existing pollution-intensive enterprises. (2) Local governments reduce environmental regulations for attracting more pollution-intensive enterprises. (3) Local governments tend to reduce environmental protection investment to save fiscal expenses. The paper's conclusions provide new policy ideas for promoting environmental protection in China, as well as served as a case for analyzing current changes in environmental protection in other countries. [ABSTRACT FROM AUTHOR]

The Institute of Geochemistry, Chinese Academy of Sciences (IGCAS) and the International Medical Geology Association (IMGA) hosted the conference with the support of Guangzhou University, China University of Geosciences (Wuhan), National University of Ireland, Galway, and Guizhou Medical University. This special issue collected 14 research papers and 1 review paper, which covered the topics of water pollution, soil pollution, and human exposure and health effects. [Extracted from the article]

This study aimed at investigating the harm from ecosystem services (ESs) according to agricultural land conversion (ALC) by using meta-analysis. The results of meta-regression showed that spatiotemporal effects had significant influences on some ES losses, and the maximum spatial impacts were relevant to Asia and Europe. Moreover, the results of ALC rate coefficients in meta-regression indicated that three large losses of ES were related to soil erosion (0.314), air pollution (0.202), and climate change (0.161). Therefore, the ALC should be done at a suitable conversion rate to reduce ES losses. Accordingly, administrators are suggested to consider careful research planning for the ALC in the process of economic development. Other strategies highlighted the importance of ALC–ES interactions for human well-being, such as measuring the pricing of goods and services based on land resources, continuously monitoring illegal ALC, and imposing taxes on unplanned ALC. [ABSTRACT FROM AUTHOR]

Recently, there has been a global shift toward clean energy sources, and wind turbines (WT) play a crucial role as one of the most popular renewable generation sources. Despite their significant potential, WTs incur high operation and maintenance (O &M) costs due to the challenging operational conditions they face, such as pollution and atmospheric discharges. These stresses reduce the life expectancy of such equipment by increasing the occurrence of failures, thereby diminishing wind farm reliability. To mitigate these failure events, the prediction of the remaining useful life (RUL) of WTs is essential. This prediction, specifically direct RUL prediction, is often made by data-driven methods. However, to achieve competitive levels of accuracy, data-driven methods found in the literature often rely on extensive datasets or high-complexity deep learning models, because historical data containing failures in WTs are scarce, which presents challenges in practical implementation. This paper introduces a novel methodology for rotor RUL prediction in WTs. This method achieved an accuracy of over 80 % using simple machine learning algorithms trained with limited data, making it easy to implement and cost-effective. It is expected that this methodology will assist energy companies in optimizing their operation and maintenance planning processes and contribute to the national energy sector's progress toward achieving global sustainable goals. [ABSTRACT FROM AUTHOR]

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

The lack of access to clean energy remains one of the major challenges in the global energy sector. Access to clean, sustainable and affordable energy, outlined in the seventh Sustainable Development Goals (SDG 7) of the United Nations, plays a crucial role in advancing health (SDG 3), as unclean cooking energy may endanger people's health by causing air pollution. However, due to endogeneity problems such as reverse causality, the health consequences of environmental pollution caused by unclean fuel usage are difficult to be scientifically and accurately evaluated. This paper aims to systematically assess the health cost of unclean fuel usage based on tackling endogeneity, using the data from Chinese General Social Survey. The ordinary least squares model, ordered regression methods, instrumental variable approach, penalized machine learning methods, placebo test, and mediation models are applied in this research. Analytical results demonstrate that households' unclean fuel use significantly damages people's health. Specifically, the use of dirty fuel leads to an average of about a one-standard-deviation decline in self-rated health, demonstrating its notable negative effect. The findings are robust to a series of robustness and endogeneity tests. The impact mechanism is that unclean fuel usage reduces people's self-rated health through increasing indoor pollution. Meanwhile, the negative effect of dirty fuel use on health has significant heterogeneity among different subgroups. The consequences are more prominent for the vulnerable groups who are female, younger, living in rural areas and older buildings, with lower socio-economic status and uncovered by social security. Therefore, necessary measures should be taken to improve energy infrastructure to make clean cooking energy more affordable and accessible as well as to enhance people's health. Besides, more attention should be paid to the energy needs of the above specific vulnerable groups faced with energy poverty. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org, CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction. [ABSTRACT FROM AUTHOR]

Green finance aims to intervene in investment and financing behaviors through financial means, so as to promote upgrading of heavily polluting industries. Whether it can reduce illegal pollution discharge behaviors is the focus of this paper. This paper is to explore the impact of green finance on illegal emissions of heavy polluting firms. Taking green finance pilot zones (GFPZ) in 2017 as a quasi-natural experiment, the paper measures illegal emission behaviors based on day-night difference of PM2.5 at the nearest atmospheric monitoring points and tests the impact of GFPZ on illegal emissions based on triple difference model (DDD). The results show that GFPZ inhibits illegal emissions. Influence path result shows that GFPZ reduces illegal emissions by increasing financing constraint, green innovation, and fulfilling social responsibility. At the same time, this paper also notes that regulatory distance has an interference effect on GFPZ. GFPZ aggravates illegal emission as heavy polluting firms — financial institution's distance is more than 40 km. GFPZ curbs illegal pollution more significantly as heavy polluting firms — environmental protection bureau distance is within 30 km. This paper aims to reveal the guiding effect of green finance on emission behavior of polluting firms and provide policy references for reducing emission and promoting green finance. [ABSTRACT FROM AUTHOR]

Based on a review of the relevant literature, this paper uses data from 70 large and medium-sized cities to test whether the purchasing behaviours of Chinese urban residents are affected by air quality. Despite the indirectness of perceptions of air quality and the non-immediacy of the impact of air quality, buyers' awareness of environmental factors plays an important role in their willingness to pay. This paper proposes that residents' awareness strengthens the influence of air quality on commercial housing prices and conducts empirical tests to examine this proposition. To overcome the deviation caused by the heterogeneity of the samples, the paper also discusses whether the above relationship varies by region, development level and political level. The empirical results show that the average annual sales price of commercial housing decreases by 0.881 units for each unit of air pollution improvement. Comprehensive analysis finds that Chinese urban residents concern the air quality near commercial housing, but the degree of influence is weak. Residents' level of awareness strengthens the influence of air quality on commercial housing prices, but the relationship varies by region, development level and political level. [ABSTRACT FROM AUTHOR]

The author reflects on the contribution of Chinese research to the field of environmental contamination and toxicology. Topics discussed include organic pollution, contamination in soils and sediments, and remediation of contaminated soils and sediments. Also being discussed are the priorities of Chinese government regarding air pollution.

Many alternative fuel technologies have been studied for the transport sector to increase its sustainability while reducing costs, greenhouse gases (GHG), and air pollution emissions. Nevertheless, conventional diesel is still the predominant fuel for heavy-duty trucks. Road freight transport consumes 25% of the world's energy and is responsible for emissions with local health impacts and the global greenhouse effect. In this context, this paper reviewed items from 2015 to 2020 to analyze the technologies available for the road freight transport regarding pollutant and GHG emissions. Results are presented in two parts: first quantitatively, quantitative data was extracted from reviewed papers and statistically treated and, second, qualitatively through a comparative chart, which shows the impact on air pollutants from the use of a different type of fuels. In general, papers are mostly concerned with particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), and hydrocarbons (HC) emissions due to its impact on public health, with a low number of papers covering GHG emissions. The trade-off between different fuels and how this process can impact emissions, sometimes increasing or decreasing specific pollutants, is discussed. According to the analyzed papers, the main characteristics that affect the pollutant emissions are, in general, the fuel oxygen content and the combustion chamber temperature. [ABSTRACT FROM AUTHOR]

Air pollution is a serious problem that affects economic development and people's health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively. [ABSTRACT FROM AUTHOR]

This paper seeks to uncover how air pollution affects China's inbound tourism using the PSM-DID approach with data collected from 285 prefecture-level cities in China from 1999 to 2017. The findings include the following: (1) Various robustness tests and two-stage least squares (2SLS) analysis using the air flow coefficient as an instrumental variable reached relatively consistent conclusions that urban air pollution leads to a significant reduction in the number of inbound tourists and that for every increase of 1 μg/m3 in the PM2.5 concentration, the number of inbound tourists will drop by approximately 80,000. (2) The findings of the heterogeneity test also show that air pollution affects inbound tourism to a significantly lower degree in provincial administrative centers and cities with richer educational resources than in other cities; cities that are better at air purification also show a less negative impact of air pollution on inbound tourism. (3) Further estimates based on the PSM-DID model confirm that policies to establish star-rated tourist attractions and to build modern transportation facilities help increase the attractiveness of tourism resources to inbound visitors and contribute to the development of inbound tourism. However, air pollution reduces the appeal of 5A-rated tourist attractions, undermines the quality of modern transportation services, and will consequently lead to a decline in the scale of China's inbound tourism. [ABSTRACT FROM AUTHOR]

Collaborative reduction of carbon and air pollutants can more efficiently achieve green technological change, industrial low-carbon transition, and high-quality economic and social development. As a typical environmental policy in China, the pilot carbon Emission Trading Scheme (ETS) has obvious advantages in achieving the collaborative reduction of carbon and air pollutants. Therefore, an evaluation of China's pilot carbon ETS from the perspective of collaborative reduction of carbon and air pollutants is performed in this paper. Compared with previous studies, first, this study innovatively uses the coupled coordination degree (CCD) model to measure the collaborative reduction level of carbon and air pollutants under different scenarios based on the panel data of China's 30 provincial-level regions during 2004–2018. Second, this study uses the DID method to evaluate the impact of China's pilot carbon ETS on the collaborative reduction of carbon and air pollutants and conducts some robustness checks and regional heterogeneity regressions. Third, this study uses the synthetic control method (SCM) further to examine the policy outcomes of the pilot carbon ETS. Scenario analysis shows that attaching importance to reducing air pollution will improve the collaborative reduction effect of carbon and air pollutants. Furthermore, the implementation of China's pilot carbon ETS exerts an effect of roughly 24.7% on reducing carbon, roughly 10.1% on reducing air pollutants, and roughly 22.0% on the collaborative reduction of carbon and air pollutants, ceteris paribus. Regional heterogeneity analysis shows that the impacts of the pilot carbon ETS are significant in all regions, except that the impact on reducing air pollutants in the central region is not significant. In addition, results from SCM indicate that the impacts of the pilot carbon ETS on the collaborative reduction of carbon and air pollutants are significantly efficient in Beijing, Tianjin, Shanghai, Hubei, and Chongqing, while not much efficient in Guangdong and Fujian. The main policy implications include strengthening the top-level design of the ETS in the collaborative reduction of carbon and air pollutants, attaching importance to the governance of air pollution, making the regional governance more targeted, and improving energy efficiency. [ABSTRACT FROM AUTHOR]

Air pollution is worsening almost everywhere in the world. According to the Health Effects Institute (HEI), more than 95% of the world population breathe polluted air, toxic to their cardiovascular and respiratory health, which caused the death of 4.2 million people worldwide in 2016. As a result, the air pollution has become one of the leading causes of death worldwide. Therefore, an early cost-efficient warning system based on precise forecasting tools must be put in place to measure and avoid the harmful effects of exposure to the main air pollutants. Thus, it is essential to obtain reliable analytical information on air quality in a specific time and place. This paper focuses on monitoring air quality using a distributed intelligence which is a cost-efficient solution that enables a flexible prediction process distributed within a network of nodes and devices using a cross-platform solution. The suggested architecture enables collaborative learning along with collective knowledge graph building and knowledge sharing using the state of the art in Internet of Things, distributed machine learning, and ontologies. The proposed architecture suggests a flexible prediction system personalized for each node based on its need of information. Similar nodes get together for collective learning which allows for resource optimization, knowledge reusability, and device interoperability. The paper describes the modeling framework of distributed intelligence monitoring and analysis system designed for urban regions. [ABSTRACT FROM AUTHOR]

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China's economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China's economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China's CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China's CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China's CO2 emissions. Results show that China's CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition. [ABSTRACT FROM AUTHOR]

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly. [ABSTRACT FROM AUTHOR]

Air pollution data provides important information on air quality, which can be used to assess the impact of atmospheric pollution on human health, the environment, and the economy, as well as to develop corresponding policies and measures to reduce pollutant emissions and improve air quality. In this paper, we propose a novel approach to improve the resolution of meteorological data via masked image modeling (PMIM) to generate high-resolution air pollution data. In order to apply the image masking modeling to process air pollution data, we convert the data format and use radial basis function visualization to generate smooth distribution maps of air pollution data. To generate high-resolution air pollution data, we design several different masking strategies and use the masked image modeling to simulate the reconstruction process from low-resolution grid data to high-resolution grid data, obtaining the reconstructed high-resolution grid images. Finally, we use the mapping relationship between the pixel colors of the reconstructed images and the air pollution data to generate high-resolution air pollution concentration data. In order to verify the effectiveness of the proposed method, we conduct comparative experiments using different masking strategies and test air pollution data of different resolutions. The results show that our method has good applicability and effectiveness in different situations. [ABSTRACT FROM AUTHOR]

This study is a contribution to the ongoing debate about the selection of the approach for carbon accounting in wood products to be used, in the future, in the national greenhouse gas inventories under the UNFCCC (United Nations Framework Convention on Climate Change). Two accounting approaches are used in this analysis: the stock-change approach and the atmospheric-flow approach. They are applied to the Portuguese Eucalyptus globulus forest sector. To achieve this objective, the fluxes of wood removed from the forest are tracked through its life cycle, which includes products manufacture (mainly pulp and paper), use and final disposal (landfilling, incineration and composting). This study develops a framework to the estimation of carbon sequestration in the forest of E. globulus, a fast growing species, more specifically, in the calculation of the conversion factors such as bark and foliage percentages and densities, used to convert wood volumes into total biomass. A mass balance approach based on real data from mills is also proposed, in order to assess carbon emissions from wood processing. The results show that E. globulus forest sector was a carbon sink, but the magnitude of the carbon sequestration differs substantially depending on the accounting approach used. The contribution of the forest ecosystem was smaller than the aggregated contribution of wood products in use and in landfills (including industrial waste), which reinforces the role that wood products play in national carbon budgets. [ABSTRACT FROM AUTHOR]

Air pollution and urban innovation are two important issues that have accompanied China's economic growth in the past decade. Based on the quasi-natural experiment of central heating in northern China in winter, this paper uses regression discontinuity design (RDD) to isolate the causal relationship between air pollution and urban innovation in China by matching urban data with multiple micro databases. Further, the possible influencing mechanism is discussed from the perspective of human capital. The results show that—air pollution significantly inhibits urban innovation—specifically, a 1% increase in air pollution significantly reduces urban innovation by 1.463%; compared with non-invention patents, air pollution has a more obvious inhibitory effect on invention patents with higher innovation capacity requirements and technology content. In terms of the impact mechanism, air pollution may undermine urban innovation by impairing healthy human capital and increasing the emigration of high-level human capital from cities. [ABSTRACT FROM AUTHOR]

To cope with the coronavirus disease (COVID-19), national or sub-national regions have carried out many powerful anti-pandemic measures such as locking down, which may improve their regional air quality. This paper examines the relation between regional air pollution and work resumption from a novel post-pandemic perspective. Using a unique panel dataset on China's detailed industrial electricity consumption, this paper does not find a positive relation between post-COVID-19 work resumption and regional air pollution during China's early-stage recovery. This result is obtained after controlling for province and date fixed effects, as well as local weather conditions. However, the positive relations are found in a particular sub-sample of large industrial enterprises and a particular sub-sample of April. These findings indicate that large industrial enterprises may recover first, and the resumption is progressing gradually. Finally, several policy implications are provided, which are essentially helpful for other countries' post-pandemic recovery. [ABSTRACT FROM AUTHOR]

This paper studies the relationship between the coal-to-gas program and air pollution in China and provides micro-evidence of the mechanism from the perspective of households. Using daily air pollution and meteorological data between January 1, 2016, and January 1, 2020, we assessed the effect of the coal-to-gas program on air pollution by introducing the regression discontinuity designs in time (RDiT). We found that the coal-to-gas program significantly improved air quality and brought significant economic benefits. In the short term, the coal-to-gas program can lead to more than 10 units of reduction in SO2, PM2.5, and AQI in the treatment group, while it can lead to more than 50 units in the long term. Using the difference-in-differences approach, we found that the coal-to-gas program has significantly reduced air pollution. Combined with the micro-panel data of the China Health and Retirement Longitudinal Study from 2011 to 2018, we found that the coal-to-gas program changes the household heating energy choices and that the probability of coal-fired heating of households in pilot areas is decreasing. The study suggests that non-clean energy in households should be further replaced to continue improving air quality. [ABSTRACT FROM AUTHOR]

Amidst the global upsurge in industrial robot deployment, there remains a notable gap in our understanding of their environmental impact. This paper explores how the introduction of industrial robots has changed air quality at both the local and neighborhood levels in China. Using the Spatial Durbin Model, we investigate the regional spillovers of PM 2.5 concentration and the diffusion of this innovative technology. Our findings reveal that the rise of robots significantly reduces air pollution in the local area, while exacerbating it in neighboring regions. This contrast is mainly because pollution-intensive industries are more inclined to relocate to neighboring regions than their cleaner counterparts, after the local use of robots increases. Throughout the process, internal costs rather than external costs dominate firms' relocation decisions. This study provides novel insights into the complex environmental externalities associated with the spread of industrial robots and highlights the critical issue of growing environmental inequality in the era of emerging technologies. [ABSTRACT FROM AUTHOR]

Public investments in subway systems are often motivated by improving local air quality. Recent studies, however, have reached different conclusions on the air quality benefits of subway investment. To reconcile these findings, this paper examines the air quality effects of all 359 subway line openings in China between 2013 and 2018. The machine learning method adopted in this paper substantially improves the consistency and precision of the estimates by purging seasonality, volatility, and the nonlinear effects of meteorological conditions in air quality data. The empirical results suggest an insignificant short-term effect and a significant long-term effect, which is expected as the adjustment of commuting mode takes time. Using the causal forest approach, the heterogeneity analysis find that a city that is experiencing rapid economic growth from a lower income level and currently has fewer subway lines is more likely to experience statistically significant improvements in air quality from a subway opening. These findings help reconcile the different findings in the literature and shed light on air pollution reduction as one of the objectives of public transit investment. [ABSTRACT FROM AUTHOR]

This study delves into the repercussions of the 2023 earthquake in Turkey, particularity its impact on air pollution. A year post-event, it is evident that scientific literature has paid limited attention to monitoring the situation. However, the release of hazardous substances, such as asbestos, lead, and other toxins, from damaged structures poses a significant threat by contaminating nearby air, soil, and water sources, thereby jeopardizing ecosystems and public well-being. The improper disposal of waste post-earthquake and the presence of mining and oil refinery sites in the region contribute to potential air pollutants. These circumstances create challenging environments conducive to the spread of respiratory diseases, with potential long-term health and social consequences. Unfortunately, existing data gaps hinder a comprehensive understanding of the situation. This paper pioneers the reporting and analysis of data regarding potential sources of air pollution resulting from the earthquake in Turkey. It also pinpoints gaps in knowledge, outlining areas that demand further investigation. To effectively prevent and mitigate air pollution risks and associated health concerns linked to earthquakes, strategic recommendations are proposed. A key suggestion is the establishment of post-disaster air pollution monitoring systems capable of swiftly identifying emerging health issues, facilitating efficient responses, and curtailing potential long-term effects of the disaster. The paper underscores the necessity for continuous health monitoring of the affected population to mitigate possible adverse impacts on human health. These strategies play a pivotal role in reducing the likelihood of air pollution, supporting emergency response and recovery initiatives, and fostering new dedicated scientific studies. [ABSTRACT FROM AUTHOR]

Atmospheric resources provide important support for human economic and social systems through their unique ecosystem service functions. Implementing the off-office auditing of atmospheric resources for leading cadres holds great strategic significance for fundamentally solve air pollution problems. Exploring the compilation of an atmospheric resource balance sheet is a necessary pre-step in the implementation of outgoing audits. In this paper, atmospheric resources are innovatively divided into atmospheric capacity resources and atmospheric quality resources from the perspective of the ecosystem service functions of atmospheric resources. The value of atmospheric resource assets is calculated by combining the environmental capacity value method and the environmental loss evaluation method. This study evaluates atmospheric ecological achievements based on the atmospheric resources balance sheet and related accounts as the data carrier and opens up the key "blocking point" of the off-office auditing of atmospheric resources. This paper takes Anhui Province as an example and applies the accounting results of the prepared atmospheric resource balance sheet to evaluate the atmospheric resource ecological achievements of leading cadres from 2016 to 2020. The results clearly reflect the ecological achievements and shortcomings of local leading cadres during their tenure. [ABSTRACT FROM AUTHOR]

Introduction: Considering the adverse carcinogenic and non-carcinogenic health effects of Volatile Organic Compounds (VOC) and specially Benzene, Toluene, Ethylbenzene and Xylenes (BTEX) which are widely used in vehicle manufacturing industry, it is clear that monitoring and control of occupational exposure to these substances is of utmost significance. More often than not the studies on occupational exposure to VOCs in vehicle manufacturing industry—specifically BTEX—have been carried out exclusively in paint shop units. The purpose of this study was to assess occupational BTEX exposure & health risk in different units of a vehicle manufacturing industry. Material and methods: Foam injection unit (FIU), gluing unit (GU), repair shop unit (RSU), molding unit (MU) as well as paint shop units (PSU) were selected for this study. Sampling and analysis were conducted based on National Institute for Occupational Safety and Health (NIOSH) 1501 guidelines by utilizing MSA personal sampling pumps set to 0.2 l/m and Gas Chromatography equipped with Flame Ionization Detector (GC-FID). Risk characterization and assessment was performed using Environmental Protection Agency Risk Assessment Information System (EPA RAIS). Results and conclusion: The results revealed that the concentration of benzene in gluing and paint shop units was above occupational exposure limits. Carcinogenic risk levels in 8 out of 10 units registered in the 1E-2 to 1E-4 range, and only two units registered a risk level below EPA's acceptable risk level (10–6). The highest risk levels were measured in gluing, paint shop and repair shop units. Furthermore, the total non-carcinogenic hazard quotient was measured higher than 1 in 8 out of 10 studied units. Considering the abundance and wide use of VOCs -most notably BTEX- and the adverse carcinogenic and non-carcinogenic health effects of these compounds, it is advised to: change the type of paint used in paint shops, improve the existing ventilation systems, develop new and more efficient ventilation systems and conduct consistent training. Highlights: In this paper, we report on the levels of occupational exposure workers encounter on a daily basis to a specific group of Volatile Organic Compounds (VOC): Benzene, Toluene, Ethylbenzene and Xylene(s) (BTEX) in various sections of a vehicle manufacturing industry and the resulting health risks. Reviewing previous studies revealed that as a result of low occupational exposure to BTEX in vehicle manufacturing industry, more often than not the studies on said compounds and the ensuing health effects were exclusively carried out in paint shop units, ignoring health risks in other units & creating the assumption that health risks in other units are negligible. The purpose of this study was to assess occupational BTEX exposure and its associated health risk in different units of a vehicle manufacturing industry, including foam injection unit (FIU), gluing unit (GU), repair shop unit (RSU), molding unit (MU) as well as paint shop units (PSU). Revealing the possibility of exposure to BTEX not only in paint shop units but also other previously disregarded units, the health risk calculated in some units such as gluing unit was higher than the EPA acceptable risk range (10-4). Moreover, cancer risk levels of molding (Unit No 6) and repair shop (Unit No 7) units registered in the 10-4 to 10-2 range despite meeting the TLV-TWA (0.174 PPM and 0.449 PPM respectively), suggesting the need for implementation of a health risk assessment in conjunction with exposure assessment. [ABSTRACT FROM AUTHOR]

This paper uses the panel data of 275 prefecture-level cities in China in 2003–2019 and spatial Durbin model to verify the impact of environmental regulation and industrial agglomeration on air pollution. This paper finds that the enforcement intensity of environmental regulations (ER) is not consistent among cities. The effects of strict ER on air pollution in local city are the inverted-U-shape curve. The effects of more stringent ER in adjacent cities j on air pollution in city i are the U-shape curve. The more stringent ER in local city may lead to the decrease in agglomeration of manufacturing sectors (AM), thereby reducing air pollution. The polluting firms may shift production from neighboring cities j with stringent ER to city i with lax ER, thereby leading to the increase in AM, which aggravate air pollution in city i. The more stringent ER in local city i may lead to the increase in agglomeration of productive service industry (AS), thereby reducing air pollution. To avoid the transfer of polluting industrial sectors, the paper suggests that the more stringent implementation of ER should become nationwide actions. [ABSTRACT FROM AUTHOR]

Every year the concern from environmental pollution increases as it affects human health. In the current paper, it is analyzed the evolution of the air quality in different cities on international level related to the reduction in mobility trend due to the emergence of the pandemic. The air pollution is studied in order to achieve healthier cities in the future and create an awareness for the citizens. The COVID-19 pandemic situation was declared during the first months of 2020 and has shown (indirectly) the effects that restrictions on human activity and road traffic have on atmospheric pollutant values. This pandemic condition derived, in most cases, in mobility restrictions in order to decrease the spread of the virus through the density of population in different places and, as a result of these restrictions, pollutant presence decreased equally. This article analyzes some of the largest cities around the world, such as Tsuen Wan district in Hong Kong (China), Los Angeles (USA), London (UK), São Paulo (Brazil), Bangalore (India), Johannesburg (South Africa), and Sydney (Australia) in order to check the different mobility restriction policies established in each location, and how those restrictions led to decrease the levels of some pollutant particles such as NO2 in the atmosphere. This should serve as a focus of awareness of the city and political interests of how it affects human activity, and those measures should be taken to reduce pollutants. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the expressway has been used as a main mode of transportation due to its function to meet traffic demand of people and thus has been given full attention. But, at the same time, it has gradually become the main cause of pollution of traffic environment. To clarify the degree of pollution caused by expressway vehicle and improve the expressway pollution diagnosis system, upon the notion of low-carbon transportation, this paper divides expressway environmental pollution into four types: air pollution, photochemical smog pollution, noise pollution, and vibration pollution, and analyzes each of them, respectively. Then, a comprehensive diagnosis model of environmental pollution caused by running vehicles will be built. This paper monitors the pollution intensity on different spots on the expressway to obtain the single-vehicle factors of various pollutants of the motor vehicles. Combined with the geographic information system, this puts forward the diagnosis methods in terms of the environmental "air pollution," "photochemical smog pollution," "noise pollution" and "vibration pollution" caused by the expressway vehicles, respectively, and further establishes a diagnosis model of vehicle pollution corresponding to the characteristics of the expressway. The result of the case study on the actual monitoring data of six expressways in Jiangsu Province shows that the pollution diagnosis values of six expressways are all between (0.4, 0.6] which symbolizes "slight pollution." The research results can provide technical support for monitoring of environmental pollution caused by expressway more comprehensively and reasonably, and provide data support for formulating effective control strategies. [ABSTRACT FROM AUTHOR]

To review and discuss recent findings on the associations between pediatric/early-life exposures to ambient air pollution and the risk of pediatric-onset inflammatory bowel diseases (IBD). A scoping review was conducted using the Peters Micah et al. framework. We searched, selected, extracted, and reviewed information from published peer-reviewed papers from three bibliographic databases, chosen to cover a broad range of disciplines. Limits on date (last decade), language, and subject were placed on the database search. The search identified 109 papers from 2010 to June 2021. After screening, we identified nine articles with data on air pollution as a risk factor for IBD, but only four epidemiologic studies directly investigated the association between air pollution and IBD development in children and young adults. These four papers show that air pollution components have different associations with pediatric IBD (pIBD) incidence. Consequently, sulfur dioxide (SO2), nitrogen dioxide (NO2), and the oxidant capacity of air pollution (Ox) were positively associated with pIBD incidence, whereas the association effects of particulate matter (PM) and ozone (O3) exposures were not clear. Despite good scientific rationale and some studies, the evidence on the role that air pollution has in IBD development is limited, highlighting the need for further investigation. Future studies should include the epidemiology of air pollutants and its sources, identifying and understanding mechanisms linking air pollution and pIBD, and identifying signatures of biological responses to air pollutants. [ABSTRACT FROM AUTHOR]

The International Maritime Organization and environmental protection agencies have formulated relevant treaties to control SOX emissions from ships for air pollution; however, the current technology application has not achieved effective online detection. To adapt to the future trend of development, this research develops a marine diesel SOX online detection system based on LabVIEW. This paper first discusses the principle of SOX detection, then designs the overall scheme of marine diesel engine SOX online detection system based on LabVIEW, and finally develops the hardware and software of the detection system. The detection system includes two parts: hardware and software. The hardware is mainly composed of a gas pretreatment module, a SOX sensor module, a USB CAN analysis module and PC and so on. Two types of software are developed: the USB CAN analysis module software and the upper computer software based on LabVIEW. With the help of USB CAN analysis module software, the problem that SOX sensor module cannot communicate with PC directly by USB interface is solved effectively. The SOX online detection system of marine diesel engine based on LabVIEW reduces the development cost and cycle, and its upper computer interface is intuitive and easy to operate. [ABSTRACT FROM AUTHOR]

Does air pollution influence traffic safety? To answer this question, this paper empirically investigates the nonlinear impact of air pollution on traffic mortality by employing a balanced panel dataset for 102 Chinese cities covering 2014–2018. Also, this paper then discusses the nonlinear mediation effects of air pollution on traffic safety to test the influential mechanism between these two variables. The following three conclusions are obtained. First, air pollution has a nonlinear impact on traffic safety: a 1 μg/m3 increase in the average annual concentration of PM2.5 could lead to a 0.0439 increase in traffic fatalities per one million people, but the negative impact of PM2.5 on air pollution will be reduced in the long run. Second, heterogeneity analysis indicates that the impact of air pollution on traffic safety in northern cities of China, cities with lower levels of economic development is more serious than that in other cities. Third, air pollution may affect traffic safety by reducing transport efficiency and affecting drivers' physiological status and cognitive abilities. [ABSTRACT FROM AUTHOR]

Advances in air monitoring methods have made it possible to analyze large-scale air pollution phenomena. Mining potential air pollution information from large-scale air pollution data is an important issue in the current environmental field. Although direct data visualization provides an intuitive presentation, the method is less applicable in long-time domains with high temporal resolution. To better meet the analysis needs of domain experts, we design a visual analysis framework based on friendly multi-view interactions and novel visual view designs. This framework can explore the spatiotemporal dynamics of multiple pollution data. In this paper, a two-stage cluster analysis method is proposed to extract possible transport patterns from large-scale pollutant transport trajectories. This method will be substantially helpful for domain experts to make relevant decisions. At the same time, the index is constructed from long-time series data at the grid point in the specific transport trajectories. This structure can help experts complete the sketch match with custom time resolution. It can assist domain experts in extracting key possible time-varying features. Finally, we verified the validity through spatial and temporal case analysis for pollutant data. [ABSTRACT FROM AUTHOR]

In order to reflect the dispersal of pollutants in non-adjacent areas and the large-scale movement of individuals, this paper proposes an epidemic model of nonlocal dispersal with air pollution, where the transmission rate is related to the concentration of pollutants. This paper checks the uniqueness and existence of the global positive solution and defines the basic reproduction number, R 0 . We simultaneously explore the global dynamics: when R 0 < 1 , the disease-free stable point is global asymptotic stability; when R 0 > 1 , the disease is uniformly persistent. Additionally, in order to approximate R 0 , a numerical method has been introduced. Illustrative examples are used to verify the theoretical outcomes and show the effect of the dispersal rate on the basic reproduction number R 0 . [ABSTRACT FROM AUTHOR]

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