Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities

Relationship Between Air Pollutants and the Incidence of Epistaxis in Yangzhou

Objectives: This project aims to explore the relationship between the air quality index (AQI), the concentration of 6 air pollutants, and the incidence of epistaxis in Yangzhou. Also, to provide reference information for the prevention and treatment of epistaxis. Methods: Data of patients with epistaxis admitted to the Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2017 to December 2021 were collected. In addition, the local AQI and the concentrations of 6 air pollutants, namely particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), were analyzed at the time of onset. Furthermore, the correlation with the incidence of epistaxis has been analyzed. Results: From 2017 to 2021, there were 24,721 patients with epistaxis aged from 0 to 17 years old while male patients were more than females. The incidence was higher in April, May, and June. There was a statistically significant difference in the number of daily epistaxis in different months and under AQI conditions (P < .05). Spearman's correlation analysis showed that there was a positive correlation between the number of daily epistaxis and the concentrations of AQI, CO, NO2, O3, PM2.5, PM10, and SO2 in Yangzhou, in which O3, PM10, and SO2 were highly correlated with the average number of daily epistaxis, and there was no obvious time lag effect of air pollutants on epistaxis. Conclusion: Epistaxis in the Yangzhou area is more common in males, mostly occurs in 0 to 17 years old, with seasonal. There was also a positive correlation between the incidence of epistaxis and air pollutants in Yangzhou. Therefore, by reducing the AQI index in daily life, and reducing the concentration of environmental pollutants in the air, the occurrence of epistaxis could be prevented and reduced to a certain extent.

Effects of air pollution on emergency visits for acute otitis media among children: a case-crossover study in Chongqing, China

Background

Many epidemiological studies have demonstrated the short-term effects of air pollution on acute otitis media (AOM) in children, but few studies have explored the association between AOM and air pollution in Chinese children. This study aimed to analyze the effects of air pollution on emergency visits for AOM among children through a time-stratified case-crossover design in Chongqing, China.

Methods

The outpatient medical records of children from nine main urban districts who presented with AOM between December 22, 2018 and December 21, 2021 were collected from the Children's Hospital of Chongqing Medical University. Data for air pollution variables, including the air quality index (AQI), particulate matter ≤ 10 μm (PM10), PM2.5, SO2, CO, NO2 and O3 from 17 monitoring sites were collected. Data for meteorological factors as confounding variables also were collected. Conditional logistic regression was used to analyze the data with single-pollutant models, multi-pollutant models, and stratified analyses.

Results

Increases in AQI, PM10, PM2.5, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in single-pollutant models and stratified analyses. Increases in PM10, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in multi-pollutant models. NO2 had the most statistically significant OR values in all models, whereas significant effects of O3 were observed only in seasonal stratification. In single-pollutant models, we found that the best lag periods were lag 0-7 for air pollution variables except for O3 and the largest OR values were 1.185 (95%CI: 1.129-1.245) for SO2 in single-pollutant models. In stratified analyses, there were no difference between groups in these statistically significant OR values through gender and age stratification, while the differences between seasons in these OR values of PM10, SO2, CO, NO2 and O3 were statistically significant. Children aged 0 years and 3-5 years represented the most susceptible population, and among the seasons, susceptibility was greater during Winter and Spring.

Conclusion

Short-term exposure to air pollution can increase emergency visits for AOM among children in Chongqing, China.

Impact of urban agglomeration construction on urban air quality-empirical test based on PSM-DID model

Urban agglomerations have become a new trend in the development of urbanization and regionalization in the world today. The construction of urban agglomerations has brought rapid economic development as well as a series of ecological and environmental problems, especially the impact on urban air quality. How to understand and evaluate the impact of urban agglomeration construction on air quality is a key issue that requires attention. City cluster construction is equivalent to a "quasi-natural experiment". This study empirically examines the impact of urban agglomeration construction on air quality in southwest China by constructing a PSM-DID model. It is found that: (1) City cluster construction has significantly improved urban air quality in urban clusters with lagging and forward-looking effects on air quality. (2) In terms of influencing factors, the level of economic development considerably improves the air quality of urban cluster cities, the industrial structure severely deteriorates the air quality of these cities, and meteorological factors highly affect their air quality. Among them, average annual urban rainfall significantly reduces urban air pollutant concentrations in urban clusters, average annual temperature significantly increases urban air pollutant concentrations, and average annual wind speed can reduce urban air pollutant concentrations. (3) Urban agglomerations are spatially heterogeneous in their impact on air quality. In this context, the topographical conditions and the level of development of urban agglomerations have a non-negligible influence on pollutant concentrations. (4) The distribution pattern of air quality pollutant concentrations in each urban agglomeration is unstable, and there are large differences in these concentrations between different urban agglomerations.

Measuring the inequalities in healthcare resource in facility and workforce: A longitudinal study in China

Objective

The study aimed to measure time trends of inequalities of the geographical distribution of health facilities and workforce in Shanghai from 2010 to 2016 and used a spatial autocorrelation analysis method to precisely detect the priority areas for optimizing health resource reallocation in metropolises like Shanghai in developing countries.

Methods

The study used secondary data from the Shanghai Health Statistical Yearbook and the Shanghai Statistical Yearbook from 2011 to 2017. Five indicators on health resources, namely, health institutions, beds, technicians, doctors, and nurses, were employed to quantitatively measure the healthcare resource in Shanghai. The Theil index and the Gini coefficient were applied to assess the global inequalities in the geographic distribution of these resources in Shanghai. Global and local spatial autocorrelation was performed using global Moran's index and local Moran's index to illustrate the spatial changing patterns and identify the priority areas for two types of healthcare resource allocation.

Results

Shanghai's healthcare resources showed decreasing trends of inequalities at large from 2010 to 2016. However, there still existed an unchanged over-concentration distribution in healthcare facilities and workforce density among districts in Shanghai, especially for doctors at the municipal level and facility allocation at the rural level. Through spatial autocorrelation analysis, it was found that there exhibited a significant spatial autocorrelation in the density distribution of all resources, and some identified priority areas were detected for resource re-allocation policy planning.

Conclusion

The study identified the existence of inequality in some healthcare resource allocations in Shanghai from 2010 to 2016. Hence, more detailed area-specific healthcare resource planning and distribution policies are required to balance the health workforce distribution at the municipal level and institution distribution at the rural level, and particular geographical areas (low–low and low–high cluster areas) should be focused on and fully considered across all the policies and regional cooperation to ensure health equality for municipal cities like Shanghai in developing countries.

What drives the spatio-temporal distribution and spillover of air quality in China’s three urban agglomerations? Evidence from a two-stage approach

Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 &lt; AQI &lt; 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.

Influence of Relative Humidity on the Characteristics of Filter Cake Using Particle Flow Code Simulation

To study the effect of air humidity on particle filtration performance, the Particle Flow Code (PFC) calculation program was used to numerically simulate the formation process of filter cake. The effects of relative air humidity on the deposition morphology, porosity and filtration resistance characteristics of the filter cake were revealed. The results show that relative humidity (RH) is mainly reflected in the density and surface viscosity of the particles. It was found that the higher the relative humidity, the higher the particle moisture content, the greater the density, and the greater the surface viscosity. With an increase in the particle density or with a decrease in the viscosity, the bridging phenomenon of particle deposition became more obvious; the dendritic deposition phenomenon became weaker; and, therefore, the filter cake structure became denser; the porosity decreased; and the total filtration resistance increased. As the humidity changed, the actual density and viscosity of the particles changed simultaneously with different degrees, which caused different variation trends of the filter cake characteristics. Three different types of particles, DM828 (Starch), PVA1788 (Polyvinyl Alcohol) and Polyacrylamide (Polyacrylic acid), were selected for comparison. For the studied PVA1788 and Polyacrylamide particles, with an increase in relative humidity, the porosity of the filter cake increased monotonously, while the total filtration resistance decreased monotonously. For DM828 particles, the cake porosity first decreased and then increased, and the total filtration resistance first increased and then decreased, with an inflection point at 30% RH. By combining these results with existing reports, three kinds of variations of the filtration performance with humidity could be determined: (1) as the humidity increased, the filtration resistance first increased and then decreased; (2) the filtration resistance decreased; and (3) the filtration resistance increased.

Analysis of spatio-temporal distribution characteristics and socioeconomic drivers of urban air quality in China

Having high spatio-temporal resolution data of pollutants is critical to understand environmental pollution patterns and their mechanisms. Our research employs the hourly average concentration data on the air quality index (AQI) and its six component pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) in 336 Chinese cities from 2014 to 2019. We analyze annual, seasonal, monthly, hourly, and spatial variations of different air pollutants and their socioeconomic factors. The results are as follows. (1) Air pollutants' concentration in Chinese cities decreased year by year during 2014-2019. Among the primary pollutants, PM_2.5 dominated pollution days, accounting for 38.46%, followed by PM₁₀. Monthly concentration curves of AQI, PM_2.5, NO₂, SO₂, and CO showed a U-shaped trend from January to December, while that of O₃ presented an inverted U-shaped unimodal pattern. Regarding daily variation, urban air quality tended to be worse around sunrise compared with sunset. (2) Chinese cities' air quality decreased from north to south and from inland to coastal areas. Recently, air quality has improved, and polluted areas have shrunk. The six pollutant types showed different spatial agglomeration characteristics. (3) Industrial pollution emissions were the main source of urban air pollutants. Energy-intensive industries, dominated by coal combustion, had the greatest impact on SO₂ concentration. A "pollution shelter" was established in China because foreign investment introduced more pollution-intensive industries. Thus, China has crossed the Kuznets U-curve inflection point. In addition, population agglomeration contributed the most to PM_2.5 concentration, increasing the PM_2.5 exposure risk and causing disease, and vehicle exhaust aggravated the pollution of NO₂ and CO. The higher China's per capita gross domestic product, the more significant the effect of economic development is on reducing pollutant concentration.

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants’ (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China.

Spatial and Temporal Characteristics of Environmental Air Quality and Its Relationship with Seasonal Climatic Conditions in Eastern China during 2015-2018

Exploring the relationship between environmental air quality (EAQ) and climatic conditions on a large scale can help better understand the main distribution characteristics and the mechanisms of EAQ in China, which is significant for the implementation of policies of joint prevention and control of regional air pollution. In this study, we used the concentrations of six conventional air pollutants, i.e., carbon monoxide (CO), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), fine particulate matter (PM_2.5), coarse particulate matter (PM₁₀), and ozone (O₃), derived from about 1300 monitoring sites in eastern China (EC) from January 2015 to December 2018. Exploiting the grading concentration limit (GB3095-2012) of various pollutants in China, we also calculated the monthly average air quality index (AQI) in EC. The results show that, generally, the EAQ has improved in all seasons in EC from 2015 to 2018. In particular, the concentrations of conventional air pollutants, such as CO, SO₂, and NO₂, have been decreasing year by year. However, the concentrations of particulate matter, such as PM_2.5 and PM₁₀, have changed little, and the O₃ concentration increased from 2015 to 2018. Empirical mode decomposition (EOF) was used to analyze the major patterns of AQI in EC. The first mode (EOF1) was characterized by a uniform structure in AQI over EC. These phenomena are due to the precipitation variability associated with the East Asian summer monsoon (EASM), referred to as the "summer-winter" pattern. The second EOF mode (EOF2) showed that the AQI over EC is a north-south dipole pattern, which is bound by the Qinling Mountains and Huaihe River (about 35° N). The EOF2 is mainly caused by seasonal variations of the mixed concentration of PM_2.5 and O₃. Associated with EOF2, the Mongolia-Siberian High influences the AQI variation over northern EC by dominating the low-level winds (10 m and 850 hPa) in autumn and winter, and precipitation affects the AQI variation over southern EC in spring and summer.

Impact of SARS-CoV-2 on Ambient Air Quality in Northwest China (NWC)

SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020.

Study on the spatial spillover effects of cement production on air pollution in China

Since 1985, China has become the largest cement producer and consumer in the world. The pollutants emitted from cement production and processing have aggravated China's pressure to conserve energy and reduce emissions. Considering the fact of cross regional transfer and capacity replacement of cement industry, this paper explores the influence of cement production on air pollution by using spatial econometric models. The results illustrate that the concentration of PM_2.5 is obviously spatially dependent and presents high-east and low-west agglomeration characteristic on a national scale. Moreover, the positive correlation between cement production and air pollution is quite obvious, the spatial spillover effects of cement production on air pollution increase progressively, and the indirect spillover effects are seven times greater than the direct spillover effects. The results also show that the phenomenon of cement industries obtaining benefits at the cost of hurting air quality in surrounding areas is the most severe in eastern China. Thus, rules should be based on local conditions when making policies in cement industries and the strong correlation between the pollution of adjacent areas should be fully considered.

Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

In China, public health awareness is growing as people get more concerned about the air quality. Based on the air quality index (AQI) of 31 provincial capital cities (2015–2018) in China, we studied the spatio-temporal correlations of air quality between cities. With spatial, temporal and spatio-temporal analysis, we systematically obtained many interesting results where the traditional analyses may be lacking. Firstly, the air quality of cities has spatial spillover and agglomeration effects and further the spatial correlation becomes higher with time. Secondly, there exists temporal correlation between the current AQI and its past values on multiple time scales, which shows certain periodicity. Thirdly, due to the changing characteristics of time, social activities and other factors affect the air quality positively. However, with the panel data model, the coefficients of spatio-temporal correlation vary for different cities.

Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O₃, wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

Inequality of female health and its relation with urbanization level in China: geographic variation perspective

Urbanization development plays a vital role in the health of modern residents; however, there have been very limited researches to specifically and comprehensively explore the relationship between urbanization level evaluating indicators (ULEIs) and female health outcomes. The mortality rate of breast cancer (BC), cervical cancer (CC), and ovarian cancer (OC) and classified urbanization factor are collected at provincial level. Stepwise regression model (SRM) and geographically weighted regression model (GWRM) are conducted to obtain spatial relationship between the mortality rate of those cancer and ULEI. Our results show that there is remarkable difference of mortality rate of BC, CC, and OC in different provinces as well as higher BC, CC, and OC distributed in northern regions. The increase of value added of primary industry (VAPI), taxi, and coal consumption has detrimental effect on BC and CC. Fuel oil consumption (FOC) ultimately results in increase of mortality rate of BC and OC, and urban fixed asset investment (UFAI) poses a risk to increase the mortality rate of OC. Contrarily, natural gas consumption (NGC) appear to mitigate mortality rate of BC. In particular, our findings demonstrate that there exist spatial differences for VAPI, FOC, NGC, taxi, and coal consumption influencing BC, CC, and OC. It is suggested that policy makers should take account of regional discrepancy and implement a sustainable urbanization development considering female health.

Estimating the Effects of Economic Agglomeration on Haze Pollution in Yangtze River Delta China Using an Econometric Analysis

Haze pollution, a serious livelihood and environmental issue, has hindered China’s economic development. This paper, based on the improved output density model, empirically analyzes spatial patterns and impact factors of haze pollution within the Yangtze River Delta from 2015 to 2017 by statistical and spatial econometric models. The study shows that: (1) The characteristics of haze pollution due to seasonal changes are obvious in the Yangtze River Delta region, and the situation has gradually improved. (2) The haze pollution has significant local agglomeration characteristics and spatial heterogeneity, demonstrated as significant low-level agglomerations in Hangzhou, Ningbo, and Taizhou, and high agglomerations in Chuzhou, Yangzhou, Zhenjiang, and Taizhou. The polluted area clusters around the provincial boundary, and its level gradually decreases from northwest to southeast. There is a significant spatial positive correlation and spatial spillover effect of intercity haze pollution, which will have a negative impact on the region and surrounding areas. (3) The population growth, research and development (R&D) investment, industrial structure, industrial smoke and dust emissions, and urban construction in the Yangtze River Delta have positive impacts on haze pollution, while factors, such as investment intensity of foreign direct investment (FDI), energy consumption and precipitation, have a negative impact on smog pollution. However, there is no Kuznets curve relationship between smog pollution and economic growth. By optimizing spatial distribution, incorporating production factors, and sharing pollution control infrastructure, this paper shows that economic agglomeration has an inhibitory effect on haze pollution.

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014⁻2017

PM_2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM_2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM_2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM_2.5 concentration changes. The results are as follows: (1) The annual average value of PM_2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM_2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM_2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m³) regulated by Chinese government. PM_2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM_2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM_2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises.

Inequalities in the geographic distribution of hospital beds and doctors in traditional Chinese medicine from 2004 to 2014

Objectives

This study identifies inequities in the provincial-level geographical distribution of traditional Chinese Medicine (TCM) hospital beds and doctors in China from 2004 to 2014. This provides policy implications of the optimal allocation of TCM health care resources.

Methods

Our study used province level data on TCM hospital beds and doctors from 2004 to 2014. These data were obtained from the China TCM Yearbook 2004–2014 and the China Statistical Yearbook 2004–2014.Global and local spatial autocorrelation was performed by using Moran’s index and the local Moran’s index to describe the spatial distribution of TCM hospital beds (doctors) as well as their density. A Gini coefficient was used to estimate inequalities in the geographic distribution of TCM hospital beds (doctors) based on their density. Correlations of the Gini coefficients between TCM hospital beds and doctors were calculated by Pearson correlation analysis.

Results

All indicators of TCM hospital beds and doctor density have increased over the past 11 years. The number of TCM hospital beds per 10,000 populations increased the fastest. Geographical clustering was not obvious in the density distribution of TCM hospital beds or doctors, as no significant spatial autocorrelation was found. Gini coefficients showed that from 2004 to 2014 the distribution of TCM hospital beds per 10,000 population and doctors per 10,000 populations were equitable between different regions. A large gap existed in the distribution inequality of TCM hospital beds (doctors) per square kilometer among different regions.

Conclusion

Targeted health policy with equitable distribution of TCM hospital beds (doctors) per square kilometer and the balance and coordination of related resources should be a priority in shaping China’s healthcare system reform.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.

An Assessment of Spatial Pattern Characterization of Air Pollution: A Case Study of CO and PM2.5 in Tehran, Iran

Statistically clustering air pollution can provide evidence of underlying spatial processes responsible for intensifying the concentration of contaminants. It may also lead to the identification of hotspots. The patterns can then be targeted to manage the concentration level of pollutants. In this regard, employing spatial autocorrelation indices as important tools is inevitable. In this study, general and local indices of Moran’s I and Getis-Ord statistics were assessed in their representation of the structural characteristics of carbon monoxide (CO) and fine particulate matter (PM2.5) polluted areas in Tehran, Iran, which is one of the most polluted cities in the world. For this purpose, a grid (200 m × 200 m) was applied across the city, and the inverse distance weighted (IDW) interpolation method was used to allocate a value to each pixel. To compare the methods of detecting clusters meaningfully and quantitatively, the pollution cleanliness index (PCI) was established. The results ascertained a high clustering level of the pollutants in the study area (with 99% confidence level). PM2.5 clusters separated the city into northern and southern parts, as most of the cold spots were situated in the north half and the hotspots were in the south. However, the CO hotspots also covered an area from the northeast to southwest of the city and the cold spots were spread over the rest of the city. The Getis-Ord’s PCI suggested a more polluted air quality than the Moran’s I PCI. The study provides a feasible methodology for urban planners and decision makers to effectively investigate and govern contaminated sites with the aim of reducing the harmful effects of air pollution on public health and the environment.