Primary Pollutants and Air Quality Analysis for Urban Air in China: Evidence from Shanghai

Analysis on Spatio-Temporal Evolution and Influencing Factors of Air Quality Index (AQI) in China

After the reform and opening up, China's economy has developed rapidly. However, environmental problems have gradually emerged, the top of which is air pollution. We have used the following methods: In view of the shortcomings of the current spatio-temporal evolution analysis of the Air Quality Index (AQI) that is not detailed to the county level and the lack of analysis of its underlying causes, this study collects the AQI of all counties in China from 2014 to 2021, and uses spatial autocorrelation and other analysis methods to deeply analyze the spatio-temporal evolution characteristic. Based on the provincial panel data, the spatial econometric model is used to explore its influencing factors and spillover effects. The research results show that: (1) From 2014 to 2021, the AQI of all counties in China showed obvious spatial agglomeration characteristics, and counties in central and western Xinjiang, as well as Beijing, Tianjin, and Hebei, were high-value agglomeration areas; (2) the change trend of the AQI value also has obvious spatial autocorrelation, and generally presents a downward trend. However, the AQI value in a small number of regions, such as Xinjiang, shows a slow decline or even a reverse rise; (3) there are some of the main factors affecting AQI, such as GDP per capita, percentage of forest cover, total emissions of SO₂, and these factors have different impacts on different regions. In addition, the increase of GDP per capita, the reduction of industrialization level, and the increase of forest coverage will significantly improve the air quality of other surrounding provinces. An in-depth analysis of the spatio-temporal evolution, influencing factors, and spillover effects of AQI in China is conducive to formulating countermeasures to improve air quality according to local conditions and promoting regional sustainable development.

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population ́s health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico's geographic characteristics do not allow proper ventilation and due to its population's density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time.Implications: This contribution presents a methodology to deal with the highly non-linear modeling of airborne particulate matter (both PM10 and PM2.5). Most linear approaches to this modeling problem are often not accurate enough when dealing with this type of data. In addition, most machine learning methods require extensive training or have problems when dealing with noise embedded in the time-series data. The proposed methodology deals with this data in three stages: preprocessing, modeling, and optimization. In the preprocessing stage, data is acquired and imputed any missing data. This ensures that the modeling process is robust even when there are errors in the acquired data and is invalid, or the data is missing. In the modeling stage, a recurrent deep neural network called LSTM (Long-Short Term Memory) is used, which shows that regardless of the monitoring station and the geographical characteristics of the site, the resulting model shows accurate and robust results. Furthermore, the optimization stage deals with enhancing the capability of the data modeling by using swarm intelligence algorithms (Ant Colony Optimization, in this case). The results presented in this study were compared with other works that presented traditional algorithms, such as multi-layer perceptron, traditional deep neural networks, and common spatiotemporal models, which show the feasibility of the methodology presented in this contribution. Lastly, the advantages of using this methodology are highlighted.

High Resolution On-Road Air Pollution Using a Large Taxi-Based Mobile Sensor Network

Traffic-related air pollution (TRAP) was monitored using a mobile sensor network on 125 urban taxis in Shanghai (November 2019/December 2020), which provide real-time patterns of air pollution at high spatial resolution. Each device determined concentrations of carbon monoxide (CO), nitrogen dioxide (NO₂), and PM_2.5, which characterised spatial and temporal patterns of on-road pollutants. A total of 80% road coverage (motorways, trunk, primary, and secondary roads) required 80-100 taxis, but only 25 on trunk roads. Higher CO concentrations were observed in the urban centre, NO₂ higher in motorway concentrations, and PM_2.5 lower in the west away from the city centre. During the COVID-19 lockdown, concentrations of CO, NO₂, and PM_2.5 in Shanghai decreased by 32, 31 and 41%, compared with the previous period. Local contribution related to traffic emissions changed slightly before and after COVID-19 restrictions, while changing background contributions relate to seasonal variation. Mobile networks are a real-time tool for air quality monitoring, with high spatial resolution (~200 m) and robust against the loss of individual devices.

Urban conditions affect soil characteristics and physiological performance of three evergreen woody species

Physiological studies conducted mainly in metropolitan areas demonstrated that urban environments generate stressful conditions for plants. However, less attention has been paid to plant response to urban conditions in small cities. Here, we evaluated to what extent the health and physiological functions of some Mediterranean urban species [Quercus ilex L., Nerium oleander L. and Pittosporum tobira (Thunb.) W.T. Aiton] were impacted by urban and peri-urban conditions in Pisa (Italy), a small medieval city with narrow streets that impede efficient public transport causing oversized private transport. Experimental period spanned from late-summer to winter in concomitance with the sharp increase in air pollutants. Climate and air quality, soil physical and chemical properties, and plant physiological traits including leaf gas exchanges, chlorophyll fluorescence and leaf pigments were assessed. In soil, the organic carbon affected aggregates and water stability and the concentrations of some micro-elements decreased in winter. Air pollutants impaired leaf gas exchanges and photochemical processes at photosystem II, depending on species, season, and urban conditions. Shrubs were more susceptible than the tree species, highlighting that the latter adapted better to pollutants along an urban-peri-urban transect in Mediterranean environments. This study gives information on the physiological adaptability of some of the most frequent Mediterranean urban species to stressful conditions and demonstrated that, even in a small city, urban conditions influence the physiology and development of vegetation, affecting the plant health status and its ability to provide key ecosystem services.

A new hybrid fuzzy time series model with an application to predict PM10 concentration

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM₁₀ concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

Interaction of interregional O3 pollution using complex network analysis

In order to improve the accuracy of air pollution management and promote the efficiency of coordinated inter-regional prevention and control, this study analyzes the interaction of O₃ in Qilihe District, Lanzhou City, China. Data used for analysis was obtained from 63 air quality monitoring stations between November 2017 and October 2018. This paper uses complex network theory to describe the network structure characteristics of O₃ pollution spatial correlation. On this basis, the node importance method is used to mine the sub-network with the highest spatial correlation in the O₃ network, and use transfer entropy theory to analyse the interaction of pollutants between regions. The results show that the O₃ area of Qilihe District, Lanzhou City can be divided into three parts: the urban street community type areas in urban areas, the township and village type areas in mountain areas and the scattered areas represented by isolated nodes. An analysis of the mutual influence of O₃ between each area revealed that the impact of O₃ on each monitoring station in adjacent areas will vary considerably. Therefore these areas cannot be governed as a whole, and the traditional extensive management measures based on administrative divisions cannot be used to replace all other regional governance measures. There is the need to develop a joint prevention and control mechanism tailored to local conditions in order to improve the accuracy and efficiency of O₃ governance.

Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review

The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO₂ and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.

Ambient air pollution and its influence on human health and welfare: an overview

Human health is closely related to his environment. The influence of exposure to air pollutants on human health and well-being has been an interesting subject and gained much volume of research over the last 50 years. In general, polluted air is considered one of the major factors leading to many diseases such as cardiovascular and respiratory disease and lung cancer for the people. Besides, air pollution adversely affects the animals and deteriorates the plant environment. The overarching objective of this review is to explore the previous researches regarding the causes and sources of air pollution, how to control it and its detrimental effects on human health. The definition of air pollution and its sources were introduced extensively. Major air pollutants and their noxious effects were detailed. Detrimental impacts of air pollution on human health and well-being were also presented.

Analysis of Spatio-temporal Characteristics and Driving Forces of Air Quality in the Northern Coastal Comprehensive Economic Zone, China

Comprehensive analysis of air quality is essential to underpin knowledge-based air quality conservation policies and funding decisions by governments and managers. In this paper, air quality change characteristics for the Northern Coastal Comprehensive Economic Zone from 2008 to 2018 were analyzed using air quality indices. The spatio-temporal pattern of air quality was identified using centroid migration, spatial autocorrelation analysis and spatial analysis in a geographic information system (GIS). A spatial econometric model was established to confirm the natural and anthropogenic factors affecting air quality. Results showed that air pollution decreased significantly. PM2.5, PM10, and O3 were the primary pollutants. The air quality exhibited an inverted U-shaped trend from January to December, with the highest quality being observed in summer and the lowest during winter. Spatially, the air quality showed an increasing trend from inland to the coast and from north to south, with significant spatial autocorrelation and clustering. Population, energy consumption, temperature, and atmospheric pressure had significant negative impacts on air quality, while wind speed had a positive impact. This study offers an efficient and effective method to evaluate air quality change. The research provides important scientific information necessary for developing future air pollution prevention and control.