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

In the backdrop of rapid and continuous deterioration in the quality of air, water and land, pollution prevention strategies have become a critical business issue to the industrial sector throughout the world. The present study aims to explore pollution prevention initiatives and their actual implementation across five different polluting industries from a large developing country's perspective. Ten sample plants (two from each industry group) were chosen from five different polluting industry groups (iron and steel, cement, pulp and paper, leather and pharmaceuticals) of a large developing economy like India. This study reveals that environmental concerns have increasingly spread among the selected polluting industries. Addressing pollution prevention practices into their business functions these industries have become more responsive to modify production process and improve the quality of the environment. However, there is a huge potential to improve it further. [ABSTRACT FROM AUTHOR]

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

Nitrogen cycling in northern temperate forest ecosystems could change under increasing atmospheric CO2 and tropospheric O3 as a result of quantitative and qualitative changes in plant litter production. At the Aspen Free Air CO2–O3 Enrichment (FACE) experiment, we previously found that greater substrate inputs to soil under elevated CO2 did not alter gross N transformation rates in the first 3 years of the experiment. We hypothesized that greater litter production under elevated CO2 would eventually cause greater gross N transformation rates and that CO2 effects would be nullified by elevated O3. Following our original study, we continued measurement of gross N transformation rates for an additional four years. From 1999 to 2003, gross N mineralization doubled, N immobilization increased 4-fold, but changes in microbial biomass N and soil total N were not detected. We observed year-to-year variation in N transformation rates, which peaked during a period of foliar insect damage. Elevated CO2 caused equivalent increases in gross rates of N mineralization (+34%) and NH immobilization (+36%). These results indicate greater rates of N turnover under elevated CO2, but do not indicate a negative feedback between elevated CO2 and soil N availability. Elevated O3 decreased gross N mineralization (−16%) and had no effect on NH immobilization, indicating reduced N availability under elevated O3. The effects of CO2 and O3 on N mineralization rates were mainly related to changes in litter production, whereas effects on N immobilization were likely influenced by changes in litter chemistry and production. Our findings also indicate that concomitant increases in atmospheric CO2 and O3 could lead to a negative feedback on N availability. [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]

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]

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 proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data. [ABSTRACT FROM AUTHOR]

Accurately predicting stock prices is crucial for reducing investment-related risks in decision-making. Contemporary challenges to financial behavior, posed by environmental issues such as pollution and climate change, have received limited attention in existing studies on capital market predictability. This paper focuses on the Shanghai Stock Exchange Composite Index (SSEC) and employs air quality and weather data from the Shanghai area as input variables. Subsequently, a hybrid prediction model is constructed by integrating Grey Relational Analysis (GRA), Wavelet Decomposition (WD), and Bidirectional Long Short-Term Memory (BiLSTM) neural networks. The objective is to achieve precise predictions of closing prices. Additionally, this study validates the feasibility of incorporating environmental factors as input variables for stock price prediction, using the Shenzhen Component Index (SZI) and Hang Seng Index (HSI) as case studies, while also assessing the applicability of the GRA-WD-BiLSTM model. The findings demonstrate that the GRA-WD-BiLSTM model exhibits superior applicability and prediction performance in stock price forecasting, with respective prediction accuracies of 95.93%, 93.02%, and 97.07% when accounting for environmental factors. The incorporation of GRA and WD contributes to enhancing single models' performance while integrating air quality and weather factors, which prove valuable in accurately predicting stock prices. The findings also indicate that the impact of regional environmental factors on local stock exchange index prices shows variability. [ABSTRACT FROM AUTHOR]

Although green technology innovation is an important means to balance the environment and economy, few studies have analyzed the employment effects of green technology innovation in developing countries. Therefore, this paper employs the panel data of 286 prefecture-level cities in China from 2006 to 2017 to empirically test the impact of various green technology innovations on urban employment. Meanwhile, from the perspectives of air quality and industrial structure optimization, the influencing mechanism of green technology innovation on employment is deeply analyzed. The results reveal that all kinds of green technology innovation can significantly increase the employment level of the city, and the promotion effect is strongest in the 2nd year after the patent application. According to the results of heterogeneity analysis, key cities of environmental protection and non-resource cities are more likely to generate positive employment effects through green technology innovation. Finally, the mediation effect test model confirms that green technology innovation can promote employment by improving air quality and causing industrial structure optimization. [ABSTRACT FROM AUTHOR]

Although numerous spatiotemporal approaches have been presented to address the problem of missing spatiotemporal data, there are still limitations in concurrently capturing the underlying spatiotemporal dependence of spatiotemporal graph data. Furthermore, most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper uses attention mechanisms to unify temporal and spatial continuity and aggregate node neighbor information in multiple directions. Furthermore, a dynamic graph convolution module is designed to capture constantly changing spatial correlations in sensors utilizing a new dynamic graph generation method with gating to transmit node information. Extensive imputation tests in the air quality and traffic flow domains were carried out on four real missing data sets. Experiments show that the ADGCN outperforms the state-of-the-art baseline. [ABSTRACT FROM AUTHOR]

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Diwali has become an occasion of air and noise pollution, and the release of particulate matter and toxic gases has chronic and acute effects on people and their environment. Thus, an air quality assessment study was done by CSIR-CSMCRI covering the pre-to-post Diwali 2021 period (5 days) in the three locations (traffic, residential, and control) of Bhavnagar. The average 24-h concentration of PM10 (380 µg/m3), PM2.5 (182.2 µg/m3), and SPM (403 µg/m3) was significantly higher during Diwali, exceeding the National Ambient Air Quality Standards (NAAQS). The concentrations of SO2 and NO2 were 121.8 µg/m3 and 102.1 µg/m3. Metals like Zn, Al, Pb, and Mn were found in higher concentrations during the study. The air quality index (AQI) was maximum on Diwali, resembling very poor air quality. More elements and oxides were detected in PM2.5 (S, Al, Mg, Ba, and Zn and their oxides) than in PM10 (Fe and S) through WDXRF. Water-soluble anions like SO42−, Cl−, and NO3− were observed during the study, with a higher SO42− (64%) on Diwali. The PM10 morphology and mapping of elements were done using SEM–EDX. Emerging contaminants, specifically phthalate groups, were detected through GCMS. The enrichment factor (EF) showed Zn and Pb originating from anthropogenic activities. The air quality data was validated using a variance test, least significance difference (LSD), correlation, and principal component analysis (PCA). This paper is the first to highlight the air quality assessment during Diwali for a western coastal place in India. It is time to implement regulations on burning firecrackers for pollution reduction, aiming to achieve a sustainable atmosphere. [ABSTRACT FROM AUTHOR]

This paper examines the effects of interventions to reduce air pollution during two international events on air quality in Beijing and its neighbor cities. Air quality data were gathered from China's Ministry of Environmental Protection, meteorological data from the China Meteorological Administration and economic data from the China Statistical Yearbook. The paper uses fixed-effect panel data models to empirically evaluate air quality improvement in Beijing and other affected cities before, during, and after the 2008 Olympic Games and the 2014 Asia-Pacific Economic Cooperation summit. Results show substantial improvement in air quality in Beijing and neighboring cities during the two events. However, some of the air quality improvement achieved reverted within a year after the games and within a week after the summit. Furthermore, the improvement achieved during the summit completely reverted and air quality deteriorated severely five days after the summit. It is also found that air quality in China, at least in the cities included in this study, gradually improved over the past 15 years or so. The findings suggest that sustainable interventions and incentive-based programs to reduce emissions from industry production and traffic are the key to maintaining the air pollution reduction achieved during the events. [ABSTRACT FROM AUTHOR]

Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model. [ABSTRACT FROM AUTHOR]

Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM 10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We first propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM 10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017–2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM 10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM 10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold. [ABSTRACT FROM AUTHOR]

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

Background: The insatiable demand for rock supplies has enticed numerous building and construction enterprises to participate in stone quarrying. However, this has had an environmental impact on air quality. This paper examines the environmental impact of quarrying on air quality in Ebonyi State, Nigeria. To achieve the main aim of the study, an objective was set to detect air pollutants at the quarry sites. A total of 220 air samples were measured from six points around the quarry locations and recorded in situ for analysis. The samples were measured three times a day (morning, afternoon, and evening) for three days. Gas monitors were used to monitor air pollutants. The generated data were subjected to completely random design (CRD) sampling techniques. The separation of means and tests was performed using Fisher's Least Significant Difference (FLSD) at a significance probability level of 5%. Results: Based on statistical analysis, the findings detected significantly higher concentration levels of particulate matter, nitrogen dioxide, hydrogen sulphide, carbon monoxide, sulphur dioxide, chlorine, volatile organic compounds, ammonia, and hydrogen cyanide in the quarry areas than the value detected in the control area. The findings also confirmed higher noise levels in the locations. It was also observed that the concentration levels of the parameters differed from point to point and at different times of the day. This really means the occurrence of a high rate of air pollution in the study locations. Conclusions: Based on the above findings, it is highly recommended that, (i) if situation is not timely addressed, it will lead to a severe environmental disaster or hazard, as nobody selects the air he or she breathes; (ii) air pollution control equipment be installed in-situ at quarry sites where free air flow is available in order to reduce gaseous (pollutant) emissions, and (iii) the seasonal effects, meteorological parameters and time that were influenced by the activities of quarry should be put in check. [ABSTRACT FROM AUTHOR]

With the rise in popularity of artificial intelligence, coupled with the growing concern over the environment, there has been a surge in the use of intelligent energy management systems. Additionally, as more buildings transition into the smart grid and, consequently, more energy and environmental data is gathered, there has been a significant increase in the number of data-driven approaches for building management systems. This paper proposes a methodology that aims to optimize the climatization and luminosity inside a building, using a genetic algorithm, a random forest, and two polynomial models. The proposed methodology enables the real-time management of the building taking into account the user needs and preferences. Air conditioner units and light systems are optimized to minimize energy costs, while also improving the air quality and considering the users' temperature and luminosity preferences. This paper shows the results achieved, by the proposed solution, in an office building case study. The promising results demonstrate the possibility of minimizing energy costs while maximizing the users' comfort. [ABSTRACT FROM AUTHOR]

The development of miniature air quality detectors makes it possible for humans to monitor air quality in real time and grid. However, the accuracy of measuring pollutants by miniature air quality detectors needs to be improved. In this paper, the PCA–RVM–NAR combined model is proposed to calibrate the measurement accuracy of the miniature air quality detector. First, correlation analysis is used to find out the main factors affecting pollutant concentrations. Second, principal component analysis is used to reduce the dimensionality of these main factors and extract their main information. Thirdly, taking the extracted principal components as independent variables and the observed values of pollutant concentrations as dependent variables, a PCA–RVM model is established by the relevance vector machine. Finally, the nonlinear autoregressive neural network is used to correct the error and finally complete the establishment of the PCA–RVM–NAR model. Root mean square error, goodness of fit, mean absolute error and relative mean absolute percent error are used to compare the calibration effect of PCA–RVM–NAR model and other commonly used models such as multiple linear regression model, support vector machine, multilayer perceptron neural network and nonlinear autoregressive models with exogenous input. The results show that, no matter which pollutant, the PCA–RVM–NAR model achieves better calibration results than other models in the four indicators. Using this model to correct the data of the miniature air quality detector can improve its accuracy by 77.8–93.9%. [ABSTRACT FROM AUTHOR]

Presents an overview of the papers contained in the 2004 issue of "Journal of Exposure Analysis and Environmental Epidemiology" that address indoor air exposure . Intervention study of students' health symptoms over time at a moisture-damaged school; Comparative study that delves into personal samples among family members with indoor and outdoor VOC concentrations in Mexico City homes; Examination of differences in VOC source emission rates using a data set of indoor and outdoor VOC measurements plus air-exchange rates in New York City and Los Angeles homes; Others.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time. [ABSTRACT FROM AUTHOR]

To deal with severe air pollution arising from rapid development, a series of air pollution control policies have been implemented in China. Previous literature has explored the short-term economic impacts of air pollution control, but the long-term economic impacts, which can better reflect the effectiveness of air pollution control, have received less attention. This paper has constructed an integrated assessment framework combining a multi-sectoral computable general equilibrium (CGE) model, air quality estimation module, and health impact module, to explore the short-term (2016) and long-term (2030) impacts of air pollution control on China's economy, by setting 2015 as the base year. The possible future air pollution reduction scenarios were set based on the Thirteenth Five-Year Plan (FYP13) proposed by the Chinese government. Our results have shown that air pollution control would harm China's economy and such adverse effects would be increased by stricter pollution reduction targets. Taking the health benefits of air pollution control into account can effectively alleviate the GDP losses, and even reverse them into economic benefits in the long term. Compared with business-as-usual (BAU) scenario (no policy constraints on SO2 and NOx emissions), the impact of air pollution control on GDP in the TAC_VIII scenario (emission reduction targets of 20% for SO2 and NOx are set every five years after 2015) would change from a loss of 1.20% in 2016 to an increase of 0.28% in 2030. Our results can provide policy implications for the optimization of China's air emissions control in the future. [ABSTRACT FROM AUTHOR]

Monitoring of atmospheric pollutants is a key point for preventive conservation since these species are known to impact the integrity of many artifacts including metals, papers, pigment and textiles. The problem can be amplified in closed environments, like in exhibition rooms and showcases, where temperature and relative humidity gradients and levels can introduce additional micro-climatic problems. One objective of the EU-SensMat project concerns the development of low cost and low visual nuisance sensors sensitive to these pollutants, making them affordable for all museums including small ones. For this purpose, the Radio-Frequency Identification (RFID) technology was considered to produce air-quality sensors based on reactive metallic dosimeter. Besides the low cost, they can be easily integrated to other existing RFID applications such as identification tasks since the interrogation is made with a commercial UHF-RFID reader. The description and the main features of these sensors are discussed in this paper. Moreover, as it will be shown, the subsequent chemical analysis of the sensitive part of the sensors provide useful information to determine the origin of the pollutants. [ABSTRACT FROM AUTHOR]

Background: Designed to set limits to air pollutant emissions, EU Directive 2008/50/EG has pushed city administrations to strengthen their commitment to environmental protection with mixed results. However, the effectiveness of these policies remains suboptimal. Within this context, this paper provides original empirical evidence on the nature and evolution of the debate over air pollution in the EU that helps readers understand how the interplay among discourse coalitions across levels of governance relates to the implementation of air pollution directives in Europe. To provide this evidence, we apply the discourse-coalition framework to a multi-level governance context by comparing debates over air pollution and examining their evolution in different contexts through the use of discourse network analysis. Results: Our results show that the debate in the EU institutions is largely dominated by pro-environmental coalitions, whereas discussions in Stuttgart and Berlin show different degrees of permeability to such arguments. Hence, the relative prominence of certain actors and levels of governance in the local discourse helps explain the extent to which debates at the local level align with the arguments put forward by the EU institutions. For their part, governmental organizations have a tendency to act as bridges between discourse coalitions. Conclusions: This novel empirical evidence provides clues that help make sense of the varying levels of implementation of EU air pollution directives at the local level. In this regard, the ability of supranational and national bodies to align their discourse with those of local organizations might facilitate a common understanding and the successful implementation of EU policies. Policymakers aiming to improve air quality at the local level may want to consider engaging actively with this debate by reinforcing the arguments of pro-environmental coalitions. They may also want to make a special effort to ensure that the messages conveyed to supranational and national bodies are also effectively conveyed to lower levels of governance. [ABSTRACT FROM AUTHOR]

Severe air pollution poses a significant threat to public safety and human health. Predicting future air quality conditions is crucial for implementing pollution control measures and guiding residents' activity choices. However, traditional single-module machine learning models suffer from long training times and low prediction accuracy. To improve the accuracy of air quality forecasting, this paper proposes a ISSA–LSTM model-based approach for predicting the air quality index (AQI). The model consists of three main components: random forest (RF) and mRMR, improved sparrow search algorithm (ISSA), and long short-term memory network (LSTM). Firstly, RF–mRMR is used to select the influential variables affecting AQI, thereby enhancing the model's performance. Next, ISSA algorithm is employed to optimize the hyperparameters of LSTM, further improving the model's performance. Finally, LSTM model is utilized to predict AQI concentrations. Through comparative experiments, it is demonstrated that the ISSA–LSTM model outperforms other models in terms of RMSE and R2, exhibiting higher prediction accuracy. The model's predictive performance is validated across different time steps, demonstrating minimal prediction errors. Therefore, the ISSA–LSTM model is a viable and effective approach for accurately predicting AQI. [ABSTRACT FROM AUTHOR]

With the rapid economic growth, air quality continues to decline. High-intensity pollution emissions and unfavorable weather conditions are the key factors for the formation and development of air heavy pollution processes. Given that research into air quality prediction generally ignore pollutant emission information, in this paper, the random forest supervised learning algorithm is used to construct an air quality prediction model for Zhangdian District with industrial waste gas daily emissions and meteorological factors as variables. The training data include the air quality index (AQI) values, meteorological factors and industrial waste gas daily emission of Zhangdian District from 1st January 2017 to 30th November 2019. The data from 1st to 31th December 2019 is used as the test set to assess the model. The performance of the model is analysed and compared with the backpropagation (BP) neural network, decision tree, and least squares support vector machine (LSSVM) function, which has better overall prediction performance with an RMSE of 22.91 and an MAE of 15.80. Based on meteorological forecasts and expected air quality, a daily emission limit for industrial waste gas can be obtained using model inversion. From 1st to 31th December 2019, if the industrial waste gas daily emission in this area were decreased from 6048.5 million cubic meters of waste gas to 5687.5 million cubic meters, and the daily air quality would be maintained at a good level. This paper deeply explores the dynamic relationship between waste gas daily emissions of industrial enterprises, meteorological factors, and air quality. The meteorological conditions are fully utilized to dynamically adjust the exhaust gas emissions of key polluting enterprises. It not only ensures that the regional air quality is in good condition, but also promotes the in-depth optimization of the procedures of regional industrial enterprises, and reduces the conflict between environmental protection and economic development. [ABSTRACT FROM AUTHOR]

Air quality index (AQI) is an essential measure of air pollution evaluation, which describes the air pollution degree and its impact on health, so the accurate prediction of AQI is significant. This paper presents an AQI prediction model based on Convolution Neural Networks (CNN) and Improved Long Short-Term Memory (ILSTM), named CNN-ILSTM. ILSTM deletes the output gate in LSTM and improves its input gate and forget gate, and introduces a Conversion Information Module (CIM) to prevent supersaturation in the learning process. ILSTM realizes efficient learning of historical data, improves prediction accuracy, and reduces the training time. CNN extracts the eigenvalues of input data effectively. This paper uses air quality data from 00:00 on January 1, 2017, to 23:00 on June 30, 2021, in Shijiazhuang City, Hebei Province, China, as experimental data sets, and compares this model with eight prediction models: SVR, RFR, MLP, LSTM, GRU, ILSTM, CNN-LSTM, and CNN-GRU to prove the validity and accuracy of CNN-ILSTM prediction model. The experimental results show the MAE of CNN-ILSTM is 8.4134, MSE is 202.1923, R2 is 0.9601, and the training time is 85.3 s. In this experiment, the performance of this model performs better than other models. [ABSTRACT FROM AUTHOR]

Although the PM2.5 concept was put forward in 1997, it came into Chinese people's sight in 2011, and became a household name in 2013 in China. Previous studies did not involve this indicator, so we need to re-examine and compare the effects of objective air quality data and subjective perception on Life satisfaction (LS). The data were collected from Xi'an, a megacity of Northwest China. Particles smaller than 2.5 μm in diameter (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and Air quality index (AQI) acquired from air quality stations were used to calculate pollutant concentrations near the vicinity of subjects' home by Geographical information system (GIS) software. In this paper, the ordinal logistic regression models are used to explain ratings of LS in terms of the objective air pollution data, social and economic variables. The results indicate that the objective air pollution data has significant and negative effects on LS of the surveyed respondents. However, when the subjective perception of air pollution is considered in the regression models, it shows that the subjective perception of air pollution has a significant negative impact on LS, while the effects of PM2.5, NO2, SO2, and AQI on the LS become insignificant. Furthermore, the research significances and policy suggestions are discussed in this paper. [ABSTRACT FROM AUTHOR]

Recently, air quality analysis based on image sensing devices has attracted much attention. Since most smoke images in real scenes have challenging variances, which is difficult for existing object detection methods. To keep real-time factory smoke under efficient and universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. We introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight detection framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the performance of the single-stage method. Experimental results show that the proposed TSSD algorithm can robustly improve the detection accuracy of the single-stage method and the model has good compatibility for image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy A P mean of our proposed TSSD model reaches 59.24 % , even surpassing the current detection model Faster RCNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), meeting the real-time requirements. This knowledge-based system has the advantages of high stability, high accuracy, fast detection speed. It can be widely used in some scenes with smoke detection requirements, such as on the mobile terminal carrier, providing great potential for practical environmental applications. [ABSTRACT FROM AUTHOR]

The paper summarizes many of the scientific achievements of Professor Sergej S. Zilitinkevich (1936–2021). It first focuses on his basic and applied atmospheric boundary layer research contributions. It then reviews their applications within research and operational numerical weather prediction and air quality modeling, showing their contribution to solving modeling problems related to extremely-stable and -unstable boundary layers. [ABSTRACT FROM AUTHOR]

The streetscape of any city reflects the natural built fabric of the street and adds to the experiential quality of that space. A sustainable streetscape ensures that the spaces are long-lasting and functional as a part of the greater sustainable eco-system. Over the past two centuries, mankind has increased the concentration of carbon dioxide (CO2) in the atmosphere from 280 to more than 380 parts per million by volume, and it is still increasing every day. If Earth continues to emit carbon without control, the surface temperature is expected to rise by 3.4 °C by the end of this century. Climate change of that magnitude would likely have serious, long-lasting, and, in many cases, devastating consequences for the planet Earth. Egypt has become one of the biggest emitters of atmospheric pollutants from the transportation sector. The level of carbon emissions and its effect on air quality are placed high on the research agenda. The transportation sector has a great impact on increasing CO2 emissions. The transportation and traffic sectors produce a quarter of the global CO2 emissions due to the heavy use of fossil fuels. This research aims to study the effects of street shape and design on CO2 production. This paper presents an analysis of road transportation in Egypt, taking Helmiet EL-Zaitoun as the main case study, with a focus on energy demand and carbon dioxide (CO2) emissions. Carbon emissions are measured using "Testo 315-3" as a measuring instrument to identify the relationship between street design and quality on the amount of carbon emissions produced. The results of this research suggest that street quality affects the amount of CO2 emissions produced. A car moving at a constant speed will produce fewer CO2 emissions than a car forced to start–stop every while because of external factors such as cracks in the roads occurring from bad streetscape elements. The presented research provides a set of guidelines to enhance the quality of the streetscape and design in order to reduce the amount of CO2 produced in the streets. [ABSTRACT FROM AUTHOR]

The traditional regional air pollution characteristics analysis method cannot obtain the best meteorological pollution factors, leading to air pollution characteristics analysis results and actual deviation. Therefore, a numerical simulation method of regional air pollution characteristics based on weather factors and improved Elman neural network algorithm is proposed. The best meteorological factors affecting air quality were obtained and the annual mean concentration of air pollutants was analyzed by Spearman rank correlation coefficient method. According to the structure of Elman neural network, an interval prediction model of atmospheric pollution concentration is designed. Based on this, control equations, turbulence model and boundary conditions are constructed. The characteristics of air pollution in a city are simulated to obtain the maximum contribution of air pollution. The results show that the simulated concentration of PM2.5and PM10 is close to the measured values, the relative error between the simulated values and the measured values is small, and the simulated atmospheric concentration is in good agreement with the measured values. Therefore, the numerical simulation analysis of pollution characteristics presented in this paper is more accurate and reliable. [ABSTRACT FROM AUTHOR]

The vehicle emissions testing programme was conducted by the UK Department of Transport in 2016 in response to emissions tampering exposed in the Volkswagen (VW) emissions scandal. The programme identified large emissions discrepancies between real-world and in-lab testing across a range of Euro 5 and Euro 6 diesel passenger vehicles. The large vehicle test fleet reflects the current challenges faced in controlling vehicle emissions. This paper presents the following findings: NOx emissions are altered due to exhaust gas recirculation mismanagement. A new Real-Life Emissions methodology is introduced to improve upon the current Real Driving Emissions standard. A large and concerning emissions divergence was discovered between the achieved NOx improvement and deterioration of CO2. The findings act as catalysts to improve vehicle emissions testing beyond standards established since the VW scandal, aiding in the development of better climate change mitigation strategies and bring tangible air quality improvements to the environment. [ABSTRACT FROM AUTHOR]

Air pollution and climate change are general problems for society. This paper proposes an integrated analysis of the Air Quality Index (AQI) and meteorological conditions in Jakarta. The column-based data integration model is applied to create integrated data of the Air Quality Index and meteorological conditions. The integrated data is then used to generate a causal graph using the PC algorithm. The causal graph reveals that there exist causal relationships between pollutants and meteorological conditions, e.g, humidity, rainfall, wind speed, and duration of sunshine affect particulate matter 10 (PM 10 ); wind speed affects sulfur dioxide (SO 2 ); temperature affects ozone (O 3 ). The historical data records that the average wind speed is decreased and the number of unhealthy days has risen. Ozone and particulate matter are two pollutants that mainly influence poor air quality in Jakarta. The integrated data is also used to train Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting. Experimental results show that LSTM using integrated data produces smaller errors for forecasting AQI and meteorological conditions. [ABSTRACT FROM AUTHOR]

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study. [ABSTRACT FROM AUTHOR]

Indoor air quality in buildings is strongly affected by chemical, physical, and biological agents. Long exposure to inadequate indoor air quality can be very dangerous for the building occupants and can lead to chronic diseases associated with the sick building syndrome (SBS). In this paper, the large presence of biological pollutants in the indoor rooms of an old building and its strict relationship with the outdoor/indoor air conditions were investigated studying Coronini Cronberg Palace Foundation, a historic house museum of the sixteenth century in Gorizia (Italy), where biological contamination affecting the artworks can soon become potentially harmful also for operators and visitors. Detailed aerobiological and microbiological analyses on organic natural materials, combined with a microclimate monitoring, were conducted to evaluate the influence of temperature and relative humidity levels within the Palace in the conspicuous growth and diffusion of microorganisms. Fungal and bacterial colonies damaging materials, mainly affected by the sudden fluctuations of hygrothermal values, were found to widely exceed Italian and international recommended levels for good air quality for both artworks and human beings. Understand their impact on human health would be strictly necessary to reduce biological risks for museum staff and cultural heritage users, but consequently to improve indoor air quality. [ABSTRACT FROM AUTHOR]

Grid monitoring is the current development direction of atmospheric monitoring. The micro air quality detector is of great help to the grid monitoring of the atmosphere, so higher requirements are put forward for the accuracy of the micro air quality detector. This paper presents a model to calibrate the measurement data of the micro air quality detector using the monitoring data of the air quality monitoring station. The concentration of six types of air pollutants is the research object of this study to establish a calibration model for the measurement data of the micro air quality detector. The first step is to use correlation analysis to find out the main factors affecting the concentration of the six types of pollutants. The second step uses Ridge Regression (RR) to select variables, find out the factors that have significant effects on the concentration of pollutants, and give the quantitative relationship between these factors and the pollutants. Finally, the predicted value of the ridge regression model and the measurement data of the micro air quality detector are used as input variables, and the Extreme Gradient Boosting (XGBoost) algorithm is used to give the final pollutant concentration prediction model. We named the combined model of ridge regression and XGBoost algorithm RR-XGBoost model. Relative Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), goodness of fit (R2), and Root Mean Square Error (RMSE) were used to evaluate the prediction accuracy of the RR-XGBoost model. The results show that the model is superior to some commonly used pollutant prediction methods such as random forest, support vector machine, and multilayer perceptron neural network in the evaluation of various indicators. The model not only has a good prediction effect on the training set but also on the test set, indicating that the model has good generalization ability. Using the RR-XGBoost model to calibrate the data of the micro air quality detector can make up for the shortcomings of the data monitoring accuracy of the micro air quality detector. The model plays an active role in the deployment of micro air quality detectors and grid monitoring of the atmosphere. [ABSTRACT FROM AUTHOR]

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is proposed to analyze multivariate longitudinal data that contains spatial location information. The method has the advantage of analyzing the relationship between curves at neighbor time points and observing the relationship between locations. We offer the spatial covariance function and use functional PCA to estimate unknown parameter functions. A detail solving process and theoretical properties are introduced. Based on the gradient descent method and leave-one-out cross-validation method, we estimate those unknown parameters and select the principal components respectively. Furthermore, compared with other four methods, the proposed method shows a better category effect on simulation studies and air quality data analysis. [ABSTRACT FROM AUTHOR]

Air pollution has emerged as a substantial threat to China's sustainable development. What remains overlooked is the resource dependence on the control of air pollution. At the same time, the resource curse may be broken by the growing digital economy, which will in turn reduce air pollution. To examine this problem, a total of 225 Chinese prefecture-level cities were selected from 2011 to 2018 to investigate the associations among resource dependence, the digital economy, and air pollution, and further, explore the mediating effect of income inequality and industrial upgrading. The primary findings are enumerated as follows: First, resource dependence is positively correlated to air pollution; second, the digital economy negatively regulates the effect of resource dependence on air pollution, that is, as the digital economy grows, air pollution decreases via resource dependence as a whole. However, a noteworthy phenomenon is that the negative moderating effect is evidently heterogeneous and is especially prominent in central China and non-resource-based cities; third, income inequality and industrial upgrading play a mediating role between resource dependence and air pollution; fourth, the digital economy can significantly influence the process where resource dependence affects air pollution via income inequality and industrial upgrading. These findings indicate the direction in the development of new strategies for different regions. Alleviating resource dependence is an important way of air pollution control. In particular, the main focus on mitigating air pollution should be adjusted to the level of income inequality and industrial structure. And the empirical results call for a strong emphasis on the digital economy for higher air quality in resource-based cities. [ABSTRACT FROM AUTHOR]

Masks are the primary tool used to prevent the spread of COVID-19 in the current pandemic. The use of masks may result in some discomfort, which may be caused by the accumulation of carbon dioxide in the inner space of the mask. This paper presents tests of carbon dioxide concentration in the inner space of the mask during work at a computer, for various flat and convex masks. Five different masks were used in the tests. Convex masks showed a greater accumulation of carbon dioxide than flat masks. The concentration of carbon dioxide was also higher for masks made of more layers. The dependence of the average values of carbon dioxide concentrations under the masks for selected people depending on the BMI and the type of mask was determined, as well as the measurements of carbon dioxide concentrations without the mask. An increase in carbon dioxide concentration was observed with increasing BMI. The development of effective self-defense tools against the virus, including masks, is essential to contain the spread of COVID-19. [ABSTRACT FROM AUTHOR]

This paper considers the level of atmospheric air pollution of the 20 largest cities in Russia in 2019–2020. The data used for the study is initially collected by a TROPOMI instrument (on the Sentinel-5P satellite), including measurements of carbon monoxide, formaldehyde, nitrogen dioxide, sulfur dioxide, and aerosol (aerosol index). The measurements were obtained using the cloud-based platform, Google Earth Engine, which presents L3 level data available for direct analysis. The Tropomi Air Quality Index (TAQI) integrates available TROPOMI measurements into a single indicator. The calculation results showed that most of the cities under consideration (15 out of 20) have a low or higher than usual level of pollution. Formaldehyde (35.7%) and nitrogen dioxide (26.4%) play the main role in the composition of pollution particles. A significant share is occupied by sulfur dioxide (16.4%). The contribution of carbon monoxide and aerosol averages 10.8 and 10.6%, respectively. Air pollution in cities is caused by both natural (wildfires, dust storms) and anthropogenic (seasonal migrations of the population, restrictions due to the COVID-19 pandemic) factors. Estimating atmospheric pollution levels in urban areas using an integral index based on remote data (such as TAQI) can be considered as a valuable information addition to existing ground-based measuring systems within the multisensory paradigm. [ABSTRACT FROM AUTHOR]

Comprehensiveness is one of the most important textual content features of online review and exhibits significant impacts on consumer's buying decisions. This paper explores the effect of air quality on review comprehensiveness by using a large-scale daily restaurant review dataset. By applying panel data and text mining method, we report an emotion-based underlying mechanism for the link between ambient air pollution levels and review comprehensiveness. Specifically, we show that air pollution levels significantly decrease review comprehensiveness, and emotion arousal mediates the relationship. Besides, our analyses reveal that the effect of the changing natural environment on reviews is asymmetrical, such that the negative relationship between air pollution levels and emotional arousal is stronger among novice reviewers. This research extends our current understanding of air pollution's psychological and behavioral effects on review writers and suggests the importance of integrating air quality information into online review management strategies. [ABSTRACT FROM AUTHOR]

Ridesharing has become popular over the last decade, bringing significant changes to residents' travel patterns and city traffic. Relevant to the growth and impact of ridesharing activities in populous urban areas are the side effects, such as air pollution and traffic congestion. Despite a number of studies on these side effects, few have focused on their underlying causes and explained the behavioral incentive. Drawing on the theory of averting behavior, this paper investigates the ridesharing choices of individuals who face but would prefer to avoid ambient air pollution. Such air pollution–averting behavior tends to increase the demand for ridesharing services, which further results in more motorized traffic and pollutant emissions. Utilizing trip records from DiDi company and data on air pollutants in Haikou, China, we find that an additional 10 μg/m3 of PM2.5 leads to an increase of 2.6% in the count of DiDi trip orders. Results also show that traffic congestion increases due to elevated DiDi usage and the effect varies with time. This could further worsen the air quality and bring other potential side effects. These empirical results offer unique insights into the impact of air pollution on ridesharing and traffic from the perspective of individual behavioral motivation. [ABSTRACT FROM AUTHOR]

Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN. [ABSTRACT FROM AUTHOR]

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities. [ABSTRACT FROM AUTHOR]

Human comfort outdoors is widely investigated, but most studies explore the comfort domains singularly. This paper aimed to evaluate human comfort in parks, verifying the importance of using a multi-domain (simultaneously evaluating thermal, visual, acoustic, and air quality) and multi-disciplinary (combining environmental and social fields) approach. A walk through a pre-defined path from one park to another was repeated twice per day on four consecutive days in June, with three participants per walk. The two investigated parks are in central Italy and were chosen because they differ in their design and spatial characteristics. Environmental data were recorded with an innovative wearable device during the whole walk, and surveys were used to assess people's perceptions of the parks. Despite observed differences in collected physical parameters, the survey's responses were similar, and different comfort domains showed dependence on each other in the two parks. Logistic regression models were developed for each park, and they revealed that the qualitative information predicted the overall comfort level more accurately than the environmental data. In detail, the models based on environmental data resulted in R2 equal to 0.126 and 0.111 in Parks 1 and 2, respectively, whereas using the survey answers increased it up to 0.820 (Park 1) and 0.806 (Park 2). This study contributes to addressing the gap in multi-domain comfort studies outdoors and confirms the importance of using multi-disciplinary and multi-domain approaches for a complete comfort analysis, supporting holistic human-biometeorology-oriented models and forecasting opportunities that can promote improvements in urban environmental quality and liveability. [ABSTRACT FROM AUTHOR]

This paper analyzed the relationship between environmental performance and tourism sector development in Mediterranean countries for the period from 2000 to 2014. For this purpose, it is adopted PM2.5 exposure as a proxy of environmental performance indicator. Economic growth, energy consumption, industrial value-added and urbanization are included in model as other explanatory variables. It is applied panel vector autoregression methodology for analyze the relationship. Main result suggests that tourism development causes deterioration in air quality. Furthermore, the U-shaped relationship between growth and pollution is confirmed for the PM2.5, and as urbanization increases, air quality deteriorates. In addition to these, the existence of a bidirectional causality relationship between tourism development and PM2.5 exposure is revealed. The analysis results imply that the nexus among tourism and environment in the Mediterranean countries is also confirmed by a specific environmental performance indicator PM2.5 exposure. [ABSTRACT FROM AUTHOR]

In this paper, the level of air quality in ten highly polluted cities in California US state is investigated by looking at persistence and seasonality properties of the time series based on fractional integration framework. Five pollutants, which are carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3) and fine and coarse particulate matters (PM2.5 and PM10), for ten selected cities, including Bakersfield, Los Angeles, and El Centro, are considered. The levels of CO, O3 and PM2.5 are above the exceedance limits for these ten cities, while the results of fractional integration show the possibility of mean reversion for PM2.5 whereas CO levels are still in non-mean reverting range. Levels of pollutants are found to be seasonal in all pollutant cases and across the cities. Since all pollutants pose serious concern to inhabitants of the cities, the California government should intensify more effort in making appropriate policy to reduce pollutant levels in the state. [ABSTRACT FROM AUTHOR]