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With the development of urban economy and the improvement of people's living standard, environmental problems have become the focus of people's attention. Among them, air pollution not only restricts the speed of economic development, but also affects the health level of the people. This study used the global spatial autocorrelation method, spatial descriptive statistics, and other index methods to explore the changing trend of spatial and temporal characteristics and influencing factors of urban air pollutants. The results showed that the air pollutions of PM10, SO2, NO2, and air pollution index (API) were mainly concentrated in spring and winter, and there was an obvious "weekend effect." Pollutants had a certain spatial dependence, and the pollution center gradually shrank from the northwest to the northeast of China. Population density and urban economic level had a negative relationship with API and main pollutants, while economic level and API had positive correlation. The results of this study provided references for scientifically coping with the characteristics of seasonal pollution transformation and strengthened the joint prevention and control mechanism of air pollution by exploring the change rules and influencing factors of air pollutants, so as to form a green and coordinated development pattern. [ABSTRACT FROM AUTHOR]

In the world, air pollution ranks among the primary sources of risk to human health and the environment. To assess the risk of impact of atmospheric pollution, a comprehensive research cycle was designed to develop a unified ecosystem for monitoring air pollution in industrial cities in Kazakhstan. Research involves analyzing data for the winter period from 20 automated monitoring stations (AMS) located in Almaty and conducting chemical-analytical studies of snowmelt water samples from 22 points to identify such pollutants as fine particulate matters, petroleum products, and heavy metals. Research includes a bio-experiment involving the cultivation of watercress on samples of melt water collected from snow cover to examine the effects of pollution on plants. In the framework of this research, we determined API based on data obtained from AMS. In order to determine the influence of atmospheric pollution on the environment, a multiple regression model was developed using machine learning algorithms to reveal the relationship between the bio-experiment data and data on pollutants of chemical-analytical research. The results revealed a wide spread of pollutants in the snow cover of the urban environment, a correlation between pollutants in the snow cover and the airspace of the city, and their negative impact on flora. [ABSTRACT FROM AUTHOR]

Long-term trends in sunshine duration in Chinese cities have been closely linked to factors caused by air pollution. To understand this impact on sunshine duration (SD), surface solar radiation from 1981 to 2020, annual PM2.5 concentration from 2012 to 2020 and air pollution index (API) data from 2013 to 2020 collected in ten representative cities in China were investigated, and the long-term relationship of SD with diffuse fraction (DF), aerosol option depth (AOD), annual PM2.5 concentration and API were analyzed. The results indicated that trends in SD varied across cities. SD decreased in seven of the ten selected cities' stations in the past 40 years, and the annual mean SD decreased from −0.03 h d−1 per decade to −0.36 h d−1 per decade—particularly in the Beijing North China Plain, Shanghai and Wuhan stations in the Yangtze River delta, where the trend coefficients were lower than −0.5. Conversely, increases in varying degrees of SD were found in Kunming (0.38 h d−1 per decade), Guangzhou and Shenyang in Southwest, South and Northeast China, respectively—with the biggest trend coefficient of 0.54 in Kunming. In addition to the SD variation, the DF in the ten city stations increased continuously from 1981 to 2010 and then declined after 2010, which is closely related to decreases in the annual PM2.5 concentration after 2012. The correlation coefficients between DF and SD ranged from −0.04 to −0.62, validating their negative relationship and the slight increasing trend in SD in recent ten years. The annual averages for SD and the DF plateaued in the 2010s due to the stringent pollution controls established by the Chinese government after 2010. Furthermore, the correlation coefficients between SD and the API ranged from −0.12 to −0.58, demonstrating a negative relationship between SD and the API. [ABSTRACT FROM AUTHOR]

Air pollution is associated with several detrimental health conditions. This study assessed comfort parameters, priority air pollutants, hydrogen sulphide (H2S), non-methane hydrocarbons (NMHCs), and volatile organic compounds (VOCs) in natural science departments in a university to understand their role in air pollutant concentrations in university environments and associated health risks. Levels of air pollutants in the departments varied. High CO2 concentrations existed in all departments with highest levels of NMHC and VOC observed in the biochemistry, microbiology and biotechnology (BMBT) department. Highest Air quality index value of 111.3 was recorded for NO2 in the BMBT department. Health risk associated with exposure to these pollutants was highest for occupants in the physiology, followed by the biodiversity, and finally BMBT department. Natural science departments seem to contribute significant amounts of H2S, NO2, NMHCs and TVOCs in university campuses. Additional ventilation and frequent monitoring of air quality in these departments are recommended. [ABSTRACT FROM AUTHOR]

Formulation of the problem. Air pollution is one of the biggest modern ecological problem, which aggravates with process of urbanization, industrial development, increasing number of vehicles, etc. So that, the article is devoted to the study the of air pollution in the largest cities of the Volyn region: Lutsk, Kowel, Novovolynsk and Volodymyr-Volynskyi. The purpose of the article is to estimate state and quality of the air in the largest cities of the Volyn region, discover the main sources of air pollution, characterize the effects of pollution on people health and environment, propose measures for solving this problem. Review of previous publications and studies. State of the air pollution in the urban areas of the Volyn region became the subject of scientific interest of many scientists. Hulai L. D., Karaim O. A. and Syniuk, A. Yu. researched ecological state of air basin and structure of sources of air pollution in the Novovolynsk City. Panasiuk M. V. and Hulai L. D. analyzed air quality in the Kovel district and Kovel City. Problems of air pollution were investigated in the studies of Kiptenko Ye. M., Kozlenko T. V. and Molchak Ya. O., Fesiuk V. O., Kartava O. F. Moreover, Poruchynska, I. V. in her publication explored ways of reducing air pollution by vehicle in the Volyn region. Methods. The most common methods in the research were analysis and synthesis, correlations and regressions, generalization and comparison. Moreover, to study the quality of air basin in the largest urban area – Lutsk City, the authors calculated index of air pollution. Results. The study defined that the main source of air pollution in the urban systems are automobiles, which produce into the atmosphere 85-95 % of all emissions. Furthermore, stationary sources such as industrial enterprises and utility companies also pollute air basin of cities with hazardous substances. However, the amount of pollutants from these sources in the Lutsk City and Volodymyr- Volynskyi City slowly decreases. The authors researched that the air basin in Lutsk City is exposed to the greatest anthropogenic loading. According to the calculation of the air pollution index, city’s air basin belongs to the quality class «polluted air». Consequences of air pollution are quite different and have impact on environment and economic complex. Besides, poor air quality can cause different diseases. To solve this problem the authors propose to reconstruct and modernize enterprises’ treatment systems, to increase the number of environmental friendly public transport, to increase the areas with greenery, to introduce green logistics in transportation. The authors concluded that air basin in the most urbanized areas of the Volyn region is quite polluted, which caused by using vehicles and functioning of manufacture. So that, it is suggested measures to reduce the amount of emission from different source of pollution. Scientific novelty and practical significance. The main preconditions and trends of modern changes in air quality of the largest cities in the Volyn region was researched for the first time. Furthermore, the authors identified the main sources of air pollution, which have affected the quality of urban air basins during recent years. In addition, index of air pollution in the Lutsk City was calculated for the period 2015-2020. The article proposes measures to improve air quality and reduce emissions from stationary and mobile sources of pollution. [ABSTRACT FROM AUTHOR]

Accurate predictions of the air quality index (AQI) is critical for pollution control and air quality warning. However, this is challenging because of the nonlinearity of data and the uncertainty between data relationships. This paper proposes a combinatorial model based on an improved adaptive dynamic particle swarm optimization (ADPSO) algorithm to optimize a bidirectional gated recurrent unit (BiGRU) neural network to predict AQI time series and capture data dependence. The ADPSO method incorporates a dynamic spatial search strategy into the standard particle swarm optimization method, allowing the parameters to be dynamically adjusted to balance global and local search capabilities, thus improving the performance and effectiveness of this optimization process. Compared to the BiGRU model, the PSO‐BiGRU model, and the radial basis neural,the results of the improved algorithm reveal that the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ADPSO‐BiGRU predicted air pollution index are smaller than the errors of the other three models. The accuracy of the ADPSO‐BiGRU prediction model is higher than that of the other models, and it aids in the development of effective regional air quality management policies to reduce the negative impacts of pollution. [ABSTRACT FROM AUTHOR]

Introduction. Air pollution is an important factor influencing the quality of life and health of peoples. It is increasing due to urbanization, development of industrial and transport complexes of cities. The same tendencies are characteristic for the state of air pollution in Lutsk. In recent years, the level of its pollution is estimated as high. And in the first half of 2018, Lutsk was among the top three cities with the highest level of air pollution in Ukraine. Although the town is not a major industrial center. The purpose of article. The purpose of the study is assessment and analyze the current level of air pollution in Lutsk for many years (2011-2019). Methods. Research methods - expeditionary (for research the ecological condition of the urban area), methods of mathematical statistics (for statistical processing of monitoring results), expert assessment (to establish the causes of pollution and environmental risks). The materials of own researches of an ecological condition of the Lutsk territory, and also results of laboratory researches of atmospheric air pollution on stationary posts in the town are used. They were conducted by the Integrated Laboratory for Environmental Pollution Observations of the Volyn Regional Center for Hydrometeorology. Results. The structure of pollutant emissions in the town is analyzed. It is established that the share of emissions from mobile sources (vehicles) increased from 61.4% in 1990 to 92.2% in 2015 and 94.6% in 2020. For the period from 2009 to the present the air pollution index (API) for the Lutsk town varies between 7.7 and 10.49 (high level of pollution). The size of the API slightly decreased until 2015, and then increased until 2018. The place of Lutsk in the ranking of the most polluted cities in Ukraine correlates with the value of the API. In 2013-14 API was relatively low - 7.99-8.55. Lutsk occupied 22 steps. Since 2016, API for the Lutsk has started to increase. The city rating rose from 9 to 7 steps. Concentrations of nitrogen dioxide, phenol and formaldehyde in the air of the town constantly exceed the maximum concentration limits. The scientific novelty is to analyze the dynamics of the content in the town air of substances whose concentrations exceed the maximum allowable, to study the causes of pollution, to develop a set of measures to reduce air pollution in Lutsk. Practical significance is determined by the possibility of using the obtained results to develop a set of measures to reduce air pollution in Lutsk. [ABSTRACT FROM AUTHOR]

Forecasting air pollution in big cities is of great importance, and there are various types of air pollution indices (APIs). In the present study, the level of air pollution in winter 2011 at four point-locations in Belgrade, Serbia, was measured using wind speed data and non-standard and standard air temperature data, measured at 10 and 2 m, respectively. Using multiple linear regression (MLR) analysis, equations to forecast the API were obtained. This forecast was verified using data from the winter of 2012/2013. The results obtained are well aligned with the monitored API and verified by the root mean square error (RMSE). It is shown that standard meteorological measurements representative of the city can accurately predict the API at individual point-locations as well as using temperature and wind speed measured at each respective location. Three locations, which measured SO2, NO2, PM10, O3 and CO, showed poor air quality for > 78% of days observed. For the fourth location, the final estimate of prevailing air quality could not be calculated due to the absence of PM10 measurements. Forecasting the API on a short-term scale can be of great help for long-term air-quality improvements. [ABSTRACT FROM AUTHOR]

It is shown that the chronic atmospheric pollution of cities in the Baikal region is caused by the combination of natural and climatic features and the industrial development of the territory. The variations in emissions of pollutants in 2008–2017 indicate an impact of the periods of growth and slowdown of macroeconomic processes in the regions and the transformation of the main production. A dominant role of thermal power engineering and processing industry as well as of road transport in the urban atmospheric pollution in the region is established. A significant increase in average annual concentrations of benzo[a]pyrene is revealed. A stable relationship was found between this growth and the population morbidity (neoplasms, respiratory diseases). The directions for reducing environmental hazards in the Baikal region cities are outlined. [ABSTRACT FROM AUTHOR]

In this paper, we study the multifractal characteristics and cross-correlation behaviour of Air Pollution Index (API) time series data through multifractal detrended cross-correlation analysis method. We analyse the daily API records of nine air pollutants of the university of Hyderabad campus for a period of three years (2013–2016). The cross-correlation behaviour has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, it is found that the cross-correlation analysis shows anti-correlation behaviour for all possible 36 bivariate time series. We also observe the existence of multifractal nature in all the bivariate time series in which many of them show strong multifractal behaviour. In particular, the hazardous particulate matter PM 2.5 and inhalable particulate matter PM 10 shows anti-correlated behaviour with all air pollutants. [ABSTRACT FROM AUTHOR]

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills. [ABSTRACT FROM AUTHOR]

Under the background of climate change, the haze days in China have increased significantly, which seriously hinders the sustainable development of society and arouses wide attention from the public. However, the researches on the effect of air pollution on the stock yield of heavy pollution enterprise in key control cities are quite limited, This paper collects the Air Quality Index (AQI) from key control cities above prefecture level in China as well as the stock yield data of these cities’ heavy pollution enterprises in December from 2014 to 2016 and through multi-discontinuities regression model, testes the effect of air pollution on the stock yield of heavy pollution enterprise. The results show that: (1) Heavy air pollution (AQI = 300) has negative influence on the stock yield. (2) There exists time effect of air pollution on stock yield. Since the year 2014, heavy air pollution has exerted significantly negative influence on the stock yield in that 2013 may be the “first year” when the Chinese society began to take air pollution seriously. This paper gives a brief discussion on the cause of it and suggests that severe air pollution should be strictly controlled. Only by facing air pollution seriously, can we eliminate air pollution with collective wisdom and concerted efforts and achieve the sustainable development of city. Being the first study to look into the effect of air pollution on stock yield in key control cities in China, this paper provides empirical reference for government supervision departments, stock investors as well as enterprises. [ABSTRACT FROM AUTHOR]

The paramagnetic properties of lichens were investigated using EPR spectroscopy and Xanthoria parietina (L.) Th. Fr. as a case study. It was found that the concentration of paramagnetic centers in lichen thalli increased as the air-pollution level increased. Possible formation mechanisms of the paramagnetic centers in lichens were discussed. The efficiency of using EPR spectroscopy to study lichens as environmental quality indicators was demonstrated. [ABSTRACT FROM AUTHOR]

How did the Sun affect the air pollution on the Earth? There are few papers about this question. This work investigates the relationship between the air pollution and solar activity by using the geophysical and environmental data during the period of 2000-2016. It is quite certain that the solar activity may impact on the air pollution, but the relationship is very weak and indirect. The Pearson correlation, Spearman rank correlation, Kendall's rank correlation, and conditional probability were adopted to analyze the air pollution index (API), air quality index (AQI), sunspot number (SSN), radio flux at wavelength of 10.7 cm (F10.7), and total solar irradiance (TSI). The analysis implies that the correlation coefficient between API and SSN is weak ( $-0.17< r<0.32$ ) with complex variation. The main results are: (1) For cities with higher air pollution, the probability of high API will be increased along with SSN, then reach to a maximum, and then decreased; (2) For cities with lower air pollution, the API has lower correlation with SSN; (3) The relationship between API and F10.7, or API and TSI are also similar as API and SSN. The solar activities take direct effect on TSI and the energetic particle flux, and indirect and long-term effect on lower atmosphere and weather near the Earth. All of these factors contribute to the air pollution on the Earth. [ABSTRACT FROM AUTHOR]

Solar energy is the primary resource for all biological, chemical and physical processes. The amount of global solar radiation is an important parameter for solar energy applications. It is common to estimate a monthly average of daily global solar radiation using different regression models. These models in turn exploit the correlation between solar radiation and various atmospheric factors. These factors are commonly derived from meteorological, geographical and climatological data that are readily available for majority of weather stations across the world. In this paper, a novel regression model that can predict location-independent daily global solar radiation is presented. The proposed exponential quadratic model captures the correlation between measured global solar radiation values, sunshine hour and Air Pollution Index for Indian cities. In addition to this, an extended study of several other regression models (e.g. linear, quadratic, exp.-linear and exp.-quadratic) is also presented. This analysis with real data from Indian cities suggests that air pollution is a more significant factor than location when predicting solar radiation. Finally, the model parameters (regression coefficients) for each model are listed out. Additionally, the generalised model equation for the best performing model is also presented. [ABSTRACT FROM PUBLISHER]

The article presents a retrospective analysis and evaluation of Shymkent city atmospheric air. The level of air pollution, according to historical data Shymkent very high, belongs to the 4 categories of quality, API = 18.6, respectively. [ABSTRACT FROM AUTHOR]

Simulations of solar radiation have become increasingly common in recent years because of the rapid global development and deployment of solar energy technologies. The effect of air pollution on solar radiation is well known. However, few studies have attempting to evaluate the potential of the air pollution index in estimating solar radiation. In this study, meteorological data, solar radiation, and air pollution index data from three sites having different air pollution index conditions are used to develop random forest models. We propose different random forest models with and without considering air pollution index data, and then compare their respective performance with that of empirical methodologies. In addition, a variable importance approach based on random forest is applied in order to assess input variables. The results show that the performance of random forest models with air pollution index data is better than that of the empirical methodologies, generating 9.1–17.0% lower values of root-mean-square error in a fitted period and 2.0–17.4% lower values of root-mean-square error in a predicted period. Both the comparative results of different random forest models and variance importance indicate that applying air pollution index data is improves estimation of solar radiation. Also, although the air pollution index values varied largely from season to season, the random forest models appear more robust performances in different seasons than different models. The findings can act as a guide in selecting used variables to estimate daily solar radiation and improve the accuracy of solar radiation estimation. [ABSTRACT FROM AUTHOR]

Background China has experienced increasingly severe levels of air pollution in the past decades, yet studies on the health effects of air pollution in China at a national study level, remain limited. This study assess the sub-chronic effect of ambient air pollution on respiratory mortality in the 32 largest Chinese cities. Methods We employ two-way fixed effects panel data analysis and monthly air pollution and mortality panel data. We estimate associations between monthly respiratory mortality and air pollution; pollution is defined as particulate matter with aerodynamic diameter <10 μm. We adjust for city characteristics, seasonality (monthly effects), and weather conditions (precipitation and temperature). We examine the associations between monthly injury mortality and air pollution to check for robustness. Results The results show positive and statistically significant associations of air pollution with respiratory mortality. During the study period (2006–2010) a 10 μg/m 3 increase in monthly PM 10 concentration is associated with a 1.05% (95% CI, 0.08–2.04%) increase in adult respiratory mortality rate. The air pollution effect is the most salient in northern cities (with central heating system) during the cold season (October–April); a 10 μg/m 3 increase in monthly PM 10 concentrations is associated with a 1.62% (95% CI, 0.22–3.46%) increase in the elderly respiratory mortality rate. There is no statistically significant association between the young adult respiratory mortality and air pollution. Conclusions The elderly respiratory mortality rate in China is positively and statistically significantly associated with air pollution. The effect is largest in northern cities during cold months when coal is burned for heating. [ABSTRACT FROM AUTHOR]

Lanzhou is one of the most aerosol-polluted cities in China. In this study, an online analyzer for Monitoring for AeRosols and GAses was deployed to measure major water-soluble inorganic ions in PM 10 at 1-hour time resolution, and 923 samples were obtained from Apr 1 to May 24, 2011. During the field campaign, air pollution days were encountered with Air Quality Index more than 100 and daily average concentration of PM 10 exceeding 150 μg/m 3 . Based on the variation of water-soluble ions and results of Positive Matrix Factorization 3.0 model execution, the air pollution days were classified as crustal species- or secondary aerosol-induced, and the different formation mechanisms of these two air pollution types were studied. During the crustal species pollution days, the content of Ca 2+ increased and was about 2.3 times higher than the average on clear days, and the air parcel back trajectory was used to analyze the sources of crustal species. Data on sulfate, trace gases and meteorological factors were used to reveal the formation mechanism of secondary aerosol pollution. The sulfur oxidation ratio (SOR) was derived from the 923 samples, and the SOR had high positive correlation with relative humidity in early summer in Lanzhou. [ABSTRACT FROM AUTHOR]

Highlights: [•] Aerosol can affect coefficients of A–P equation to estimate solar radiation. [•] Logarithmic model performed best, according to MBE, MABE, MPE, MAPE, RMSE and NSE. [•] Parameters of A–P model can be adjusted by API, geographical position and altitude. [•] A general equation to estimate solar radiation was established in China. [ABSTRACT FROM AUTHOR]

Abstract: We developed a risk-based, multi-pollutant air quality health index (AQHI) reporting system in Hong Kong, based on the Canadian approach. We performed time series studies to obtain the relative risks of hospital admissions for respiratory and cardiovascular diseases associated with four air pollutants: sulphur dioxide, nitrogen dioxide, ozone, and particulate matter with an aerodynamic diameter less than 10 μm (PM10). We then calculated the sum of excess risks of the hospital admissions associated with these air pollutants. The cut-off points of the summed excess risk, for the issuance of different health warnings, were based on the concentrations of these pollutants recommended as short-term Air Quality Guidelines by the World Health Organization. The excess risks were adjusted downwards for young children and the elderly. Health risk was grouped into five categories and sub-divided into eleven bands, with equal increments in excess risk from band 1 up to band 10 (the 11th band is ‘band 10+’). We developed health warning messages for the general public, including at-risk groups: young children, the elderly, and people with pre-existing cardiac or respiratory diseases. The new system addressed two major shortcomings of the current standard-based system; namely, the time lag between a sudden rise in air pollutant concentrations and the issue of a health warning, and the reliance on one dominant pollutant to calculate the index. Hence, the AQHI represents an improvement over Hong Kong's existing air pollution index. [Copyright &y& Elsevier]

Abstract: The Air Quality Health Index (AQHI) was originally developed in Canada. However, little is known about its validity in communicating morbidity risks. We aimed to establish the AQHI in Shanghai, China, and to compare the associations of AQHI and existing Air Pollution Index (API) with daily mortality and morbidity. We constructed the AQHI as the sum of excess total mortality associated with individual air pollutants, and then adjusted it to an arbitrary scale (0–10), according to a time-series analysis of air pollution and mortality in Shanghai from 2001 to 2008. We examined the associations of AQHI with daily mortality and morbidity, and compared these associations with API from 2005 to 2008. The coefficients of short-term associations of total mortality with particulate matter with an aerodynamic diameter less than 10μm (PM10), PM2.5 and nitrogen dioxide (NO2) were used in the establishment of AQHI. During 2005–2008, the AQHI showed linear non-threshold positive associations with daily mortality and morbidity. A unit increase of the PM10-AQHI was associated with a 0.90% [95% (confidence interval, CI), 0.43 to 1.37], 1.04% (95%CI, 0.04 to 2.04), 1.62% (95%CI, 0.39 to 2.85) and 0.51% (95%CI, 0.09 to 0.93) increase of current-day total mortality, hospital admissions, outpatient visits and emergency room visits, respectively. The PM2.5-AQHI showed quite similar effect estimates with the PM10-AQHI. In contrast, the associations for API were much weaker and generally statistically insignificant. The AQHI, compared with the existing API, provided a more effective tool to communicate the air pollution-related health risks to the public. [Copyright &y& Elsevier]

In terms of the chaos theory, the phase-space-reconstruction method has been employed to describe the multi-dimensional phase space for the time series of air pollution index (API) during the past 10 years in Lanzhou, northwest China. The mutual information and Cao method were used to determine the reconstruction parameters, and the characteristic quantities including the Lyapunov exponent and the correlation dimension were calculated respectively. As a result, the correlation dimensions were fractioned, and the maximum Lyapunov exponent ( λ) > 0. It shows that these presented the obvious chaotic characteristics that resulted from the evolution of non-linear chaotic dynamic system in the time series of air pollution index over the past 10 years. In the meanwhile, three or even four main dynamic variables were discussed here that could effectively interpret the changes of air pollution index time series and their causes. Some reasonable preventive countermeasures were thus put forward. These findings might provide a scientific basis for probing further into the regional complexity and evolution of the time series of air pollution index. [ABSTRACT FROM AUTHOR]

Abstract: In this study, the Daily Air Pollution Index (DAPI) of 81 cities in China from June 2004 to June 2007 was used to study the air pollution of city clusters. The cluster analysis results show that the DAPI of 71 cities in central and eastern China is divided into two regimes, the north regime and the south regime along the Qinling Mountain and its eastern extension to Huaihe River. The demarcation line between the two DAPI regimes duplicates the demarcation line of the watersheds of the northern and southern climate regions and the DAPI were statistically negatively correlated to the amount of rainfall, suggesting that precipitation could play an important role in determining the two regimes. The two DAPI regimes are further divided into seven city clusters mainly associated with emissions of air pollutants and geographical distributions, supported by remote sensing data of aerosol optical depth (AOD), NO2 and emission inventories of air pollutants in China in 2006 reported in the literature. DAPI of three city clusters in the north regime exhibited a similar seasonal trend, which was different from the four city clusters in the south regime. Outbreak of dust storms in spring and an increase in emissions due to space-heating impacted the DAPI of the northern city clusters. Although the consumption of electricity decreased in the southern city clusters in the fall and winter, the reported high DAPI were the result of less rainfall and poor dispersion. Seven cities in western China were classified into two groups associated with their unique nature environments. Three cities were not allocated to any of the city clusters. [Copyright &y& Elsevier]

It seems evident that the understanding of the complex temporal evolution dynamic characteristics of air pollution indexes (APIs) can contribute to developing advanced techniques for air pollution forecasting. In this work, monofractal and multifractal methods have been successfully used to characterize the temporal fluctuations of APIs in Shanghai. It shows that APIs in Shanghai are characterized by scale invariance, long range dependence and multifractal scaling. Introducing a kind of variable window sizes method, we analyze the temporal evolution of the monofractal and multifractal behaviors of APIs. Some information about the temporal evolution dynamics of APIs is revealed. Our study suggests that the variable window sizes method can be help the analysis of air pollution time series temporal evolution dynamical mechanisms. This work could play an important role in the research of air pollution. [ABSTRACT FROM AUTHOR]

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices' influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its' real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects. [ABSTRACT FROM AUTHOR]

Abstract: Lanzhou is one of the most polluted cities in China. Sulfur dioxide (SO2) concentrations have shown evident seasonal variability. Pollution was generally within the second-level criterion (<0.15 mg m−3) in spring, summer, and fall but was much higher than the second-level criterion and sometimes reaches mid-level pollution (API>200) in winter. Meteorological conditions (low winds, stable stratification) were found to be important for SO2 pollution. Observational and modeling studies conducted in the present study showed a close connection between static stability and SO2 pollution in Lanzhou, China, during winter. [Copyright &y& Elsevier]

Abstract: Concentrations of total suspended particles (TSP) and PM10 in Lanzhou China have been kept high for the past two decades. Data collected during the intensive observational period from October 1999 to April 2001 show high TSP and PM10 concentrations. Starting from November, the PM10 pollution intensifies, and reaches mid to high alert level of air pollution, continues until April next year, and is at low alert level in the summer. In the winter and spring, the TSP concentration is 2–10 times higher than the third-level criterion of air quality (severe pollution). Effects of intrinsic factors (sources of pollution) and remote preconditions (propagation of dust storms) for severe PM10 and TSP pollution in Lanzhou are analyzed. [Copyright &y& Elsevier]

Abstract: Lanzhou is one of the major industrial cities in northwest China, the capital of Gansu Province, and located at a northwest-to-southeast oriented valley basin with elevation about 1500–1600-m. Due to topographic and meteorological characteristics, Lanzhou is one of the most polluted cities in China. Meteorological conditions (low winds, stable stratification especially inversion), pollutant sources and sinks affect the air quality. Lanzhou government carried out afforestation and pollutant-source reduction (closing several heavy industrial factories) to improve the air quality for the past two decades. In this study, effect of afforestation on reducing the NO X concentration is investigated numerically using RAMS-HYPACT model. [Copyright &y& Elsevier]

This study investigates the region-dependent anthropogenic weekly variation in air pollutants and its relationship with the meteorological conditions over China for the summers of 2001–2005. Spectral analysis was applied to the local daily observations of PM10 (aerosol particulate matter with a diameter <10μm) mass concentrations and precipitation from 31 ground stations, reanalysis estimates of regional atmospheric variables, and satellite retrievals of clouds. Our analysis shows that the 6–8-day variance of PM10 concentrations from the periodogram is closely correlated with the mean PM10 concentration, which may depend on the size (population) and geographical setting of a city, its prevailing climatic conditions, and the type/degree of human activities. We define normalized variance as the ratio of the 6–8-day to 2–14-day variance of PM10 concentrations, possibly indicating the relative anthropogenic signal to the noise of natural weather variability. The normalized variance of PM10 concentrations has a distinct regional rainfall distribution from that of the mean PM10 concentration in China. As compared to regions with lower normalized variance of PM10 concentrations, the regions with higher normalized variance generally show higher normalized variance of rainfall events, 1000hPa wind speeds, sea-level pressure, size spectrum and phase of cloud particles, cloud optical depth, and cloud top pressure. Our results confirm the presence of the interaction between PM10 and the meteorological conditions in the boundary layer, and suggest a possible link of cloud formation to PM10 on a weekly scale. [Copyright &y& Elsevier]

Abstract: In this study the frequencies of PM10 (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM10 concentration in the high-frequency period is higher with an extent 0.009–0.066mgm−3 than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM10 concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM10 concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM10 is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM10 concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM10 concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM10 concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM10 pollution concentration is usually caused by floating dust. [Copyright &y& Elsevier]

Abstract: An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, and , respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for , , and . Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied. [Copyright &y& Elsevier]

The variation in the concentration of outdoor air pollutants during the COVID-19 lockdown was studied in Jakarta, Indonesia. The term lockdown was replaced by large-scale social restrictions (PSBB) in Indonesia by more flexible regulations to save the economy. Data on five air pollutants, namely, PM 10 , SO 2 , CO, O 3 , and NO 2 , from five monitoring stations located in five regions in Jakarta (West, East, Central, North, and South Jakarta) were utilized. We analyzed the changes in the concentrations of outdoor air pollutants before lockdown from January 1 to April 9, 2020, and during lockdown from April 10 to June 4, 2020. Overall, the CO concentration (39.9%) demonstrated the most significant reduction during lockdown, followed by NO 2 (7.5%) and then SO 2 (5.7%). However, we unexpectedly found that during lockdown, the PM 10 concentration in Jakarta increased by 10.9% due to the southwest monsoon during the seasonal change in Jakarta. Among the five cities in Jakarta, East and Central Jakarta experienced the maximum improvement in their air quality, whereas North Jakarta had the least air quality improvement. To the best of our knowledge, this research is the first to study the effect of lockdown on outdoor air quality improvement in Indonesia using ground-level measurement data. The findings of the study provide additional strategies to the regulatory bodies for the reduction of temporal air pollutants in Jakarta, Indonesia, by restricting people mobility as a supplementary initiative. • CO concentration reduced by 39.9% averagely with highest reduction in Central Jakarta. • NO 2 and SO 2 have shown considerable decline by 7.5% and 5.7% respectively. • PM 10 concentrations in five cities in DKI Jakarta were increased by 10.9% during lockdown. • The East and Central Jakarta have experienced maximum improvements in air quality. • North Jakarta has experienced minimum improvements in air quality. [ABSTRACT FROM AUTHOR]

The air quality is one of the major concerns in the urban environment due to the rapid changes in pollutant emissions driven by complex and intensive human activities. Therefore, quantification of the urban air quality has become an essential need for both urban residents and authorities to quickly assess air quality conditions. To reach this aim, the air quality index (AQI) is the primary way to better understand the urban air quality. However, the varied AQIs in different countries are difficult to directly compare due to the varied calculation methods. Thus, this research presents an updated review of the major AQIs worldwide by dividing them into two categories: single- and multi-contaminant-oriented AQIs. Single-contaminant-oriented AQIs are based on the maximum value of individual pollutants and are applied in most countries with location-dependent standards, such as the United States, China, the United Kingdom and New South Wales, Australia. However, these may greatly underestimate the impact of multiple contaminants, be difficult to dynamically update or to be compared internationally. Moreover, multi-contaminant-oriented AQIs are available in the literature, which consider the combined effects of exposure to multiple contaminants. Among these AQIs, arithmetic pollutant aggregation simply integrates pollutants in a linear or nonlinear way, and weighted pollutant aggregation further assigns varied weights from different perspectives. Combining the advantages and disadvantages of the existing AQIs, the general air quality health index (GAQHI) is proposed as a pollutant-aggregated, local health-based AQI paradigm suitable for the present complex multi-contaminant situation. It provides a direction for the construction of a more accurate, consistent and comparable AQI system and can help both researchers and governments improve human well-being and achieve sustainable development. • Multi-contaminant air pollution is common in cities and endangers human health. • Single-contaminant-oriented AQIs are applied in most countries. • GAQHI is proposed as a pollutant-aggregated, local health-based AQI paradigm. • GAQHI is more sensitive in reflecting complex multiple contaminants. [ABSTRACT FROM AUTHOR]

The aim of this contribution is both to demonstrate and to explore the general assessment pertaining to the effects of atmospheric factors on human health and general wellbeing. While humans are aware of such effects, particularly individually, their concrete and synergetic effects with the human physiological system are, comparatively, not well comprehended. In human biometeorological studies and approaches, the aforementioned effects are analyzed in terms of their effect pathways, and the development of single or complex approaches. Recurrently in the existing literature, such approaches are mostly defined and, respectively, targeted as indexes. The evaluation and assessment of similar factors and parameters that present related effects were subsequently put together and quantified. This approach is described as 'effective complexes' or components. The most well-known examples are the thermal complex, air pollution complex (which can be divided into the biological (pollen) and anthropogenic (air pollutants) factors), actinic complex, and finally, the recent or rapid weather changes complex. Most of the approaches focus on the negative effects consequential to the established criteria ranging from empirical outputs, to epidemiological studies. As a result, the presented approach does not only include the negative effects or implications on humans. Instead, it also highlights the neutral and positive effects which were acknowledged by the research. The approach deals furthermore with the combined effects of different complexes or components and incorporates different weightings of the factors based on the disclosed effects. In addition, seasonal and exposure factors are deliberated upon to differentiate annual variability factors. Finally, the presented approach builds upon a way in which to cogitate how the complex interactions associated to weather and climate can be quantified in a more appropriate way in the context of human health. The approach aims to be applied for a specific weather forecast enabling the communication and balance between human health factors, and also more encompassing climatic analysis. [ABSTRACT FROM AUTHOR]

Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels' U statistic. Also, the model's validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality. [ABSTRACT FROM AUTHOR]