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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]

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]

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]

Understanding public awareness of environmental problems is vital for effectively formulating sustainable policies. This paper aims to investigate the impacts of two perspectives—external air pollution and individual health status—on public awareness by leveraging panel data from two waves of the China Family Panel Studies (CFPS) conducted between 2018 and 2020. The model integrates provincial-level PM2.5 concentration indicators and SO2, PMs, and NOx emissions. The results reveal a significantly positive correlation between air pollution and public awareness of environmental problems in China. Additionally, this study examines the impact of self-assessed health shock by categorizing it into worse and better health. The influence of better health is insignificant. Conversely, when individuals experience worse health, they may perceive it as a psychological loss, leading to a significant increase in public awareness of environmental problems. This study provides valuable insights for mitigating air pollution and reinforcing public health in developing countries. [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 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]

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

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]

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]

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]

Air pollution is the result of comprehensive evolution of a dynamic and complex system composed of emission sources, topography, meteorology and other environmental factors. The establishment of spatiotemporal evolution model is of great significance for the study of air pollution mechanism, trend prediction, identification of pollution sources and pollution control. In this paper, the air pollution system is described based on cellular automata and restricted agents, and a Swarm Intelligence based Air Pollution SpatioTemporal Evolution (SI-APSTE) model is constructed. Then the spatiotemporal evolution analysis method of air pollution is studied. Taking Henan Province before and after COVID-19 pandemic as an example, the NO2 products of TROPOMI and OMI were analysed based on SI-APSTE model. The tropospheric NO2 Vertical Column Densities (VCDs) distribution characteristics of spatiotemporal variation of Henan province before COVID-19 pandemic were studied. Then the tropospheric NO2 VCDs of TROPOMI was used to study the pandemic period, month-on-month and year-on-year in 18 urban areas of Henan Province. The results show that SI-APSTE model can effectively analyse the spatiotemporal evolution of air pollution by using environmental big data and swarm intelligence, and also can establish a theoretical basis for pollution source identification and trend prediction. [ABSTRACT FROM AUTHOR]

For centuries, scientists have observed nature to understand the laws that govern the physical world. The traditional process of turning observations into physical understanding is slow. Imperfect models are constructed and tested to explain relationships in data. Powerful new algorithms can enable computers to learn physics by observing images and videos. Inspired by this idea, instead of training machine learning models using physical quantities, we used images, that is, pixel information. For this work, and as a proof of concept, the physics of interest are wind-driven spatial patterns. These phenomena include features in Aeolian dunes and volcanic ash deposition, wildfire smoke, and air pollution plumes. We use computer model simulations of spatial deposition patterns to approximate images from a hypothetical imaging device whose outputs are red, green, and blue (RGB) color images with channel values ranging from 0 to 255. In this paper, we explore deep convolutional neural network-based autoencoders to exploit relationships in wind-driven spatial patterns, which commonly occur in geosciences, and reduce their dimensionality. Reducing the data dimension size with an encoder enables training deep, fully connected neural network models linking geographic and meteorological scalar input quantities to the encoded space. Once this is achieved, full spatial patterns are reconstructed using the decoder. We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0.02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0.93. [ABSTRACT FROM AUTHOR]

Using ArcGIS to analyze satellite derived PM2.5 estimates, this paper obtains the average concentration and maximum concentration of fine particulate matter (PM2.5) in China's 31 provinces from 2002 to 2015. We adopt fixed effects model and spatial Durbin model to investigate the association between PM2.5 and perinatal mortality rates. The results indicate that PM2.5 has a significantly positive association with perinatal mortality rates. A 1% increase of log-transformed average concentration and maximum concentrations of PM2.5 is associated with 1.76‰ and 2.31‰ increase of perinatal mortality rates, respectively. In spatial econometrics analysis, we find PM2.5 has significant spatial autocorrelation characteristics. The concentrations of log-transformed average and maximum PM2.5 increase 1% is associated with a 2.49% increase in a 2.49‰ and 2.19‰ increase of perinatal mortality rates, respectively. The potential mechanism is that air pollution has an impact on infant weight to impact perinatal mortality rates. [ABSTRACT FROM AUTHOR]

Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide (NO), nitrogen dioxide ( N O 2 ) and particulate matter ( P M 10 and P M 2.5 ) concentrations generically exhibit heavy tails and are asymptotically well approximated by q-exponential distributions with a given width parameter λ . We observe that the power-law parameter q and the width parameter λ vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the (q , λ) plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity. [ABSTRACT FROM AUTHOR]

The development of industry has brought about the pollution of the atmospheric environment. Pollution is harmful to people's health. Realizing the real-time monitoring of atmospheric environmental quality parameters can improve the above-mentioned effects. China's existing environmental monitoring systems focus on the accuracy of the system hardware itself for assessment, lack of data analysis and forecasting and early warning, and cannot provide managers and ordinary people with decision-making and activity guidance. This paper develops an IPV6-based high-spatial–temporal precision air pollutant monitoring and early warning platform. The feasibility of the system is verified through networking tests, operation tests, and early warning tests. Through actual data analysis and comparison, it is concluded that the monitoring system has field feasibility, and the atmospheric environment monitoring for the target observation area has achieved the desired observation function. This system integrates GIS technology and B/S architecture to analyze changes in the regional environment to provide support for regional environmental air quality management. The forecast and early warning module constructed by combining the weight method of the influence of different input factors on the environmental quality index and minute-level observations can provide technical support for the government to improve the level of supervision. [ABSTRACT FROM AUTHOR]

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe. [ABSTRACT FROM AUTHOR]

An estimation of the impact of climatic conditions—measured with an index that combines temperature and humidity, the IPTCC—on the hospitalizations and deaths attributed to SARS-CoV-2 is proposed. The present paper uses weekly data from 54 French administrative regions between March 23, 2020 and January 10, 2021. Firstly, a Granger causal analysis is developed and reveals that past values of the IPTCC contain information that allow for a better prediction of hospitalizations or deaths than that obtained without the IPTCC. Finally, a vector autoregressive model is estimated to evaluate the dynamic response of hospitalizations and deaths after an increase in the IPTCC. It is estimated that a 10-point increase in the IPTCC causes hospitalizations to rise by 2.9% (90% CI 0.7–5.0) one week after the increase, and by 4.1% (90% CI 2.1–6.4) and 4.4% (90% CI 2.5–6.3) in the two following weeks. Over ten weeks, the cumulative effect is estimated to reach 20.1%. Two weeks after the increase in the IPTCC, deaths are estimated to rise by 3.7% (90% CI 1.6–5.8). The cumulative effect from the second to the tenth weeks reaches 15.8%. The results are robust to the inclusion of air pollution indicators. [ABSTRACT FROM AUTHOR]

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25–April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved. [ABSTRACT FROM AUTHOR]

The wall pressure fluctuations induced by a subsonic circular jet on a rigid flat plate have been investigated considering two jets with different exit section diameters at the same Mach number. The analysis is aimed at completing the series of papers presented by the authors on the interaction between a subsonic jet and infinite tangential flat plate where the exit Mach number was the only parameter of the jet flow that was varied. In order to analyse other effects out of the Mach number, two configurations with different nozzle exhaust diameters were explored with the objective of isolating the Reynolds number effect keeping fixed the exit Mach number. The nozzle exhaust diameters are 12 mm and 25.4 mm and the instrumented flat plate, installed parallel to the jet flow, is moved at different radial distances from the jet axis. The pressure footprint on the plate has been measured in the stream-wise direction by means of a pair of flush-mounted pressure transducers, providing point-wise pressure signals. Wall pressure fluctuations have been characterised in terms of spectral and statistical quantities. The effect of Reynolds is evidenced and possible scaling relationships that account for the Reynolds dependence are proposed. Implications for modeling the spectral coherence have been considered by the application of the Corcos' model and the effect of the jet Reynolds number on the model coefficients is analyzed. [ABSTRACT FROM AUTHOR]

Urban air pollution represents a global problem, since everyday many mutagenic and carcinogens compounds are emitted into the atmosphere, with consequent adverse health effects on humans and biota. Specifically, particulate matter air pollution was associated with increased risks in human mortality and morbidity. In this paper, we analyse the genomic effects on human lymphocytes of different concentrations of annual Turin PM2.5 extract by an in vitro micronuclei assay. Samplings were collected from an urban meteorological-chemical station positioned in Turin (Italy), one of the most polluted cities in Europe. PM2.5 sampled on filters was used for organic extraction in monthly pools and successively aggregated to produce a mixture representative for a full year PM2.5 collection. Lymphocytes were exposed to four concentrations of PM2.5: 5, 10, 15 and 20 μg/mL and micronuclei, nucleoplasmic bridges and nuclear buds were scored. With respect to controls, PM2.5 significantly increased the frequencies of all analysed biomarkers at all tested concentrations, whereas the CBPI index was significantly reduced only at the concentration of 20 μg/mL. Such in vitro effects can both to stimulate local authorities to adopt efficient measures for air pollution mitigation and to improve human monitoring to detect early precancer lesions. [ABSTRACT FROM AUTHOR]

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) is related to behavioural and development disorders, anxiety is related to particulate matter (PM10), O3 and SO2, and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used. [ABSTRACT FROM AUTHOR]

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks. [ABSTRACT FROM AUTHOR]

In many emerging nations, rapid industrialization and urbanization have led to heightened levels of air pollution. This sudden rise in air pollution, which affects global sustainability and human health, has become a significant concern for citizens and governments. While most current methods for predicting air quality rely on shallow models and often yield unsatisfactory results, our study explores a deep architectural model for forecasting air quality. We employ a sophisticated deep learning structure to develop an advanced system for ambient air quality prediction. We utilize three publicly available databases and real-world data to obtain accurate air quality measurements. These four datasets undergo a data cleaning to yield a consolidated, cleaned dataset. Subsequently, the Fused Eurasian Oystercatcher-Pathfinder Algorithm (FEO-PFA)—a dual optimization method combining the Eurasian Oystercatcher Optimizer (EOO) and Pathfinder Algorithm (PFA)—is applied. This method aids in selecting weighted features, optimizing weights, and choosing the most relevant attributes for optimal results. These optimal features are then incorporated into the Multiscale Depth-wise Separable Adaptive Temporal Convolutional Network (MDS-ATCN) for the ambient Air Quality Prediction (AQP) process. The variables within MDS-ATCN are further refined using the proposed FEO-PFA to enhance predictive accuracy. An empirical analysis is performed to compare the efficacy of our proposed model with traditional methods, underscoring the superior effectiveness of our approach. The average cost function is reduced to 5.5%, the MAE to 28%, and the RMSE to 14% by the suggested method, according to the performance research conducted with regard to all datasets. [ABSTRACT FROM AUTHOR]

Abstract Understanding public awareness of environmental problems is vital for effectively formulating sustainable policies. This paper aims to investigate the impacts of two perspectives—external air pollution and individual health status—on public awareness by leveraging panel data from two waves of the China Family Panel Studies (CFPS) conducted between 2018 and 2020. The model integrates provincial-level PM2.5 concentration indicators and SO2, PMs, and NOx emissions. The results reveal a significantly positive correlation between air pollution and public awareness of environmental problems in China. Additionally, this study examines the impact of self-assessed health shock by categorizing it into worse and better health. The influence of better health is insignificant. Conversely, when individuals experience worse health, they may perceive it as a psychological loss, leading to a significant increase in public awareness of environmental problems. This study provides valuable insights for mitigating air pollution and reinforcing public health in developing countries.

Abstract 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.

Air pollution poses a persuasive threat to global health, demonstrating widespread detrimental effects on populations worldwide. Exposure to pollutants, notably particulate matter with a diameter of 2.5 µm (PM2.5), has been unequivocally linked to a spectrum of adverse health outcomes. A nuanced understanding of the relationship between them is crucial for implementing effective policies. This study employs a comprehensive investigation, utilizing the extended health production function framework alongside the system generalized method of moments (SGMM) technique, to scrutinize the interplay between air pollution and health outcomes. Focusing on a panel of the top twenty polluted nations from 2000 to 2021, the findings yield substantial insights. Notably, PM2.5 concentration emerges as a significant factor, correlating with a reduction in life expectancy by 3.69 years and an increase in infant mortality rates by 0.294%. Urbanization is found to increase life expectancy by 0.083 years while concurrently decreasing infant mortality rates by 0.00022%. An increase in real per capita gross domestic product corresponds with an improvement in life expectancy by 0.21 years and a decrease in infant mortality rates by 0.00065%. Similarly, an elevated school enrollment rate is associated with a rise in life expectancy by 0.17 years and a decline in infant mortality rates by 0.00032%. However, a higher population growth rate is found to modestly decrease life expectancy by 0.019 years and slightly elevate infant mortality rates by 0.000016%. The analysis reveals that per capita greenhouse gas emissions exert a negative impact, diminishing life expectancy by 0.486 years and elevating infant mortality rates by 0.00061%, while per capita energy consumption marginally reduces life expectancy by 0.026 years and increases infant mortality rates by 0.00004%. Additionally, economic volatility shock presents a notable decrement in life expectancy by 0.041 years and an increase in infant mortality rates by 0.000045%, with inflationary shock further exacerbating adverse health outcomes by lowering life expectancy by 0.70 years and elevating infant mortality rates by 0.00025%. Moreover, the study scrutinizes the role of institutional quality, revealing a constructive impact on health outcomes. Specifically, the institutional quality index is associated with an increase in life expectancy by 0.66% and a decrease in infant mortality rates by 0.0006%. Extending the analysis to examine the nuanced dimensions of institutional quality, the findings discern that economic institutions wield a notably stronger positive influence on health outcomes compared to political and institutional governance indices. Finally, the results underscore the pivotal moderating role of institutional quality in mitigating the deleterious impact of PM2.5 concentration on health outcomes, counterbalancing the influence of external shocks, and improving the relationships between explanatory variables and health outcome indicators. These findings offer critical insights for guiding evidence-based policy implications, with a focus on fostering resilient, sustainable, and health-conscious societies. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the associations between cardiorespiratory fitness (CRF), long-term air pollution exposure and biochemical markers of oxidative status and inflammation. This is a cross-sectional investigation focusing on biochemical markers of oxidative status and inflammation. Participants were Caucasian (N = 1188; age 18–65 years) who lived for at least 5 years in a high air-polluted (Moravian-Silesian; MS) or low air-polluted (South Bohemia; SB) region of the Czech Republic. Healthy runners and inactive individuals were recruited. A multiple regression analysis was used to explain the relationship between multiple independent variables (CRF, trunk fat mass, sex, socioeconomic status, and region (MS region vs. SB region) and dependent variables (oxidative status, inflammation). CRF, trunk fat mass, age and sex significantly predicted almost all selected markers of oxidative status and inflammation (except GSSG, GSH/GSSG and BDNF). Participants living in the MS region presented significantly higher GPx (by 3.1%) and lower BDNF values (by 4.5%). All other investigated biochemical markers were not significantly influenced by region. We did not find meaningful interactions between long-term air-pollution exposure versus markers of oxidative status and inflammation. However, we showed various significant interactions with sex, age, CRF and body composition. The significant association of living in the high air polluted MS region with the BDNF level warrants further attention. [ABSTRACT FROM AUTHOR]

Jakarta Greater Area (JGA) has encountered recurrent challenges of air pollution, notably, high ozone levels. We investigate the trends of surface ozone (O3) changes from the air quality monitoring stations and resolve the contribution of meteorological drivers in urban Jakarta (2010–2019) and rural Bogor sites (2017–2019) using stepwise Multi Linear Regression. During 10 years of measurement, 41% of 1-h O3 concentrations exceeded Indonesia' s national threshold in Jakarta. In Bogor, 0.1% surpassed the threshold during 3 years of available data records. The monthly average of maximum daily 8-h average (MDA8) O3 anomalies exhibited a downward trend at Jakarta sites while increasing at the rural site of Bogor. Meteorological and anthropogenic drivers contribute 30% and 70%, respectively, to the interannual O3 anomalies in Jakarta. Ozone formation sensitivity with satellite demonstrates that a slight decrease in NO2 and an increase in HCHO contributed to declining O3 in Jakarta with 10 years average of HCHO to NO2 ratio (FNR) of 3.7. Conversely, O3 increases in rural areas with a higher FNR of 4.4, likely due to the contribution from the natural emission of O3 precursors and the influence of meteorological factors that magnify the concentration. [ABSTRACT FROM AUTHOR]

Long-term exposures to environmental factors including airborne as well as noise pollutants, are associated with cardiovascular risk. However, the influence of environmental pollution on the young population is controversial. Accordingly, we aimed to investigate the relationships between long-term exposures to different environmental factors and major cardiovascular and inflammatory parameters and biomarkers in young, healthy subjects. Representative sample of permanent residents of two cities differing in air and noise pollution levels, aged 15–21 years, were recruited. Krakow and Lublin, both located in southern Poland, were chosen in relation to their similarities in demographic and geopolitical characteristics, but differences in air pollution (higher in Krakow) and noise parameters (higher in Lublin). A total of 576 subjects were studied: 292 in Krakow and 284 in Lublin. All subjects underwent health questionnaire, blood pressure measurements and biomarker determinations. Inflammatory biomarkers, such as CRP, hs-CRP, fibrinogen as well as homocysteine were all significantly higher in subjects living in Krakow as opposed to subjects living in Lublin (for hsCRP: 0.52 (0.32–0.98) mg/l vs. 0.35 (0.22–0.67) mg/l; p < 0.001). Increased inflammatory biomarker levels were observed in Krakow in both male and female young adults. Interestingly, significant differences were observed in blood pressure between male and female subjects. Males from Krakow had significantly higher mean systolic blood pressure (127.7 ± 10.4 mm/Hg vs. 122.4 ± 13.0 mm/Hg; p = 0.001), pulse pressure (58.7 ± 8.9 mm/Hg vs. 51.4 ± 12.3 mm/Hg; p < 0.001) and lower heart rate (p < 0.001) as compared to males living in Lublin. This was not observed in young adult females. Long-term exposure to environmental factors related to the place of residence can significantly influence inflammatory and cardiovascular parameters, even in young individuals. Interestingly, among otherwise healthy young adults, blood pressure differences exhibited significant variations based on biological sex. [ABSTRACT FROM AUTHOR]

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches. [ABSTRACT FROM AUTHOR]

Radiative balance, local climate, and human health are all significantly influenced by aerosol. Recent severe air pollution over Lahore, a city in Pakistan calls for more thorough research to determine the negative impacts brought on by too many aerosols. To study regional aerosol characteristics and their differences from various aspects, in-depth and long-term (2007–2020) investigations of the columnar aerosol properties over the urban environment of Lahore were carried out by using AERONET data. The Aerosol Optical Depth (AOD400) and Angstrom Exponent (AE400–870) vary from low values of 0.10 to a maximum value of 4.51 and from 0.03 to 1.81, respectively. The huge differences in the amount of AOD440 as well as AE440–870 show the large fluctuation of aerosol classes because of various sources of their emission. During the autumn and winter seasons, the decreasing trend of the optical parameters of aerosols like Single Scattering Albedo (SSA) and Asymmetry Parameter (ASY) with increasing wavelength from 675 to 1020 nm indicates the dominance of light-absorbing aerosols (biomass burning (BB) and industrial/urban (UI). Due to the long-distance dust movement during spring, summer, and autumn, coarse mode particles predominated in Lahore during the study period. Dust type (DD) aerosols are found to be the dominant one during spring (46.92%), summer (54.31%), and autumn (57.46%) while urban industry (BB/UI) was dominant during the winter season (53.21%). During each season, the clean continental (CC) aerosols are found to be in negligible amounts, indicating terrible air quality in Lahore City. The present research work fills up the study gap in the optical properties of aerosols in Lahore and will help us understand more fully how local aerosol fluctuation affects regional climate change over the urban environment of Lahore. [ABSTRACT FROM AUTHOR]

Abstract For the lack of precise monitoring and accurate assessment models for air quality, this paper fully considers such constraints and establishes an evaluation model of air pollution emission level to evaluate the air pollution emission level of Wuhan—a city in central China. The model uses entropy weight method including important indicators of air pollution into the integrated optimization of air quality assessment, laying the basis for sources of pollution and the reasonable and effective city development. The total emissions of air pollution for Wuhan shows a gradual upward trend over time, mainly coming from industrial pollution. The government can reduce air pollution by focusing on detecting major polluting industries, promoting industrial technological progress and innovation, and strengthening the effective implementation of emission trading system.

When analyzing health data in relation to environmental stressors, it is crucial to identify which variables to include in the statistical model to exclude dependencies among the variables. Four meteorological parameters: temperature, ultraviolet radiation, precipitation, and vapor pressure and four outdoor air pollution parameters: ozone ( O 3 ), nitrogen dioxide ( NO 2 ), particulate matter ( P M 2.5 , P M 10 ) were studied on a daily basis for Baden-Württemberg (Germany). This federal state covers urban and rural compartments including mountainous and river areas. A temporal and spatial analysis of the internal relationships was performed among the variables using (a) cross-correlations, both on the grand ensemble of data as well as within subsets, and (b) the Local Indications of Spatial Association (LISA) method. Meteorological and air pollution variables were strongly correlated within and among themselves in time and space. We found a strong interaction between nitrogen dioxide and ozone, with correlation coefficients varying over time. The coefficients ranged from negative correlations in January (−0.84), April (−0.47), and October (−0.54) to a positive correlation in July (0.45). The cross-correlation plot showed a noticeable change in the correlation direction for O 3 and NO 2 . Spatially, NO 2 , P M 2.5 , and P M 10 concentrations were significantly higher in urban than rural regions. For O 3 , this effect was reversed. A LISA analysis confirmed distinct hot and cold spots of environmental stressors. This work examined and quantified the spatio-temporal relationship between air pollution and meteorological conditions and recommended which variables to prioritize for future health impact analyses. The results found are in line with the underlying physico-chemical atmospheric processes. It also identified postal code areas with dominant environmental stressors for further studies. [ABSTRACT FROM AUTHOR]

Urban trees' biomonitoring of pollutants such as polycyclic aromatic hydrocarbons (PAHs) yields pertinent and useful data for air pollution management. The aim of this study was to biomonitor PAHs in pine (Pinus eldarica Medw.) trees in the city of Isfahan and identify their sources. In total, 34 samples of outer bark of the trees were collected and their contents of 16 EPA PAHs were analyzed. With a median value of 136.3 ng/g, the total PAH contents in tree barks varied from 53.4 to 705.2 ng/g. The average values of the diagnostic ratios for Ant/(Ant + Phe), Flu/(Flu + Py), BaA/(BaA + Chr) and IP/(IP + BP) were 0.19, 0.49, 0.45 and 0.49, respectively, revealing the PAHs majority source of pyrogenic. Meanwhile, principal component analysis showed two major types of PAHs sources including pyrogenic (fossil fuel combustion and industrial activities) and petrogenic (uncombusted) sources. The average ratio An/(An + Phe) and Flu/(Flu + Py) in bark samples was close to their relevant ratios in ambient air which demonstrated the potential use of this approach for biomonitoring of PAHs. [ABSTRACT FROM AUTHOR]

This study aimed to create Greenhouse Gas - Air Pollution Interactions and Synergies (GAINS)-Korea, an integrated model for evaluating climate and air quality policies in Korea, modeled after the international GAINS model. GAINS-Korea incorporates specific Korean data and enhances granularity for enabling local government-level analysis. The model includes source-receptor matrices used to simulate pollutant dispersion in Korea, generated through CAMx air quality modeling. GAINS-Korea's performance was evaluated by examining different scenarios for South Korea. The business as usual scenario projected emissions from 2010 to 2030, while the air quality scenario included policies to reduce air pollutants in line with air quality and greenhouse gas control plans. The maximum feasible reduction scenario incorporated more aggressive reduction technologies along with air quality measures. The developed model enabled the assessment of emission reduction effects by both greenhouse gas and air pollutant emission reduction policies across 17 local governments in Korea, including changes in PM2.5 (particulate matter less than 2.5 μm) concentration and associated benefits, such as reduced premature deaths. The model also provides a range of visualization tools for comparative analysis among different scenarios, making it a valuable resource for policy planning and evaluation, and supporting decision-making processes. [ABSTRACT FROM AUTHOR]