Exposure inequality assessment for PM2.5 and the potential association with environmental health in Beijing

Spatiotemporal patterns of surface ozone exposure inequality in China

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted toward the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration. To summarize the above conclusions, the management of the atmospheric environment in Henan Province still has a long way to go, and the formulation of relevant policies should be adapted to local conditions, taking into account the spatial scale effect of the impact of different influencing factors on PM2.5.

Identifying the Environmental Determinants of Lung Cancer: A Case Study of Henan, China

Lung cancer has become one of the most prevalent cancers in the last several decades. Studies have documented that most cases of lung cancer are caused by inhaling environmental carcinogens while how external environmental factors lead to individual lung cancer is still an open issue as the pathogenesis may come from the combined action of multiple environmental factors, and such pathogenic mechanism may vary from region to region. Based on the data of lung cancer cases from hospitals at the county level in Henan from 2016 to 2020, we analyzed the response relationship between lung cancer incidence and physical ambient factors (air quality, meteorological conditions, soil vegetation) and socioeconomic factors (occupational environment, medical level, heating mode, smoking behavior). We used a Bayesian spatio-temporal interaction model to evaluate the relative risk of disease in different regions. The results showed that smoking was still the primary determinant of lung cancer, but the influence of air quality was increasing year by year, with meteorological conditions and occupational environment playing a synergistic role in this process. The high-risk areas were concentrated in the plains of East and Central Henan and the basin of South Henan, while the low-risk areas were concentrated in the hilly areas of North and West Henan, which were related to the topography of Henan. Our study provides a better understanding of the environmental determinants of lung cancer which will help refine existing prevention strategies and recognize the areas where actions are required to prevent environment and occupation related lung cancer.

Socio-demographic characteristics and inequality in exposure to PM2.5: A case study in the Sichuan basin, China

The Chengyu Metropolitan Area (CYMA), located in the Sichuan Basin, is an unevenly developed region with high PM_2.5 concentrations and a population of approximately 100 million. Although exposure inequality in air pollution has received increasing concern, no related research has been carried out in the CYMA to date. In this work, we used the concentration index to assess inequality of PM_2.5 population-weighted exposure in the CYMA among different subgroups, including age, education, gender, occupation and GDP per capita in the city of residence. Our findings revealed that the non-disadvantaged subgroups (people aged 15-64, people with senior and higher education, people with high-income occupations and residents of cities with high GDP per capita) had a higher PM_2.5 exposure in the CYMA, with the concentration indices of -0.03 (95% CI: 0.064, -0.001), -0.14 (95% CI: 0.221, -0.059), -0.15 (95% CI: 0.238, -0.056) and -0.27 (95% CI: 0.556, 0.012), opposite to previous studies in developed countries such as the United States and France. In addition, exposure differences among cities were much larger than those among populations in the CYMA. These findings may benefit the government in identifying disproportionately exposed subgroups in developing regions, and suggest that related measures should initially be carried out for cities exposed to high PM_2.5 concentrations rather than for populations exposed to high PM_2.5 concentrations.

Lung protective effects of dietary malate esters derivatives from Bletilla striata against SiO2 nanoparticles through activation of Nrf2 pathway

Objective

To study the protective activities of the dietary malate esters derivatives of Bletilla striata against SiO₂ nanoparticles-induced A549 cell lines and its mechanism action.

Methods

The components were isolated and elucidated by spectroscopic methods such as 1D NMR and 2D NMR. And MTT assays was used to tested these components on the A549 cell survival rates and ROS or proteins levels were detected by Western blotting.

Results

A new glucosyloxybenzyl 2-isobutylmalate (a malate ester derivative), along with 31 known compounds were isolated and identified from n-BuOH extract of EtOH extract of B. striata. Among them, compounds 3, 4, 11, 12 and 13 possessed noteworthy proliferative effects for damaged cells, with ED₅₀ of 14.0, 13.1, 3.7, 11.6 and 11.5 µmol/L, respectively, compared to positive control resveratrol (ED₅₀, 14.7 µmol/L). Militarine (8) prominently inhibited the intracellular ROS level, and increased the expression of Nrf2 and its downstream genes (HO-1 and γ-GCSc). Furthermore, Nrf2 activation mediates the interventional effects of compound 8 against SiO₂ nanoparticles (nm SiO₂)-induced lung injury. Moreover, treatment with compound 8 significantly reduced lung inflammation and oxidative stress in nm SiO₂-instilled mice. Molecular docking experiment suggested that 8 bound stably to the HO-1 protein by hydrogen bond interactions.

Conclusion

The dietary malate esters derivatives of B. striata could significantly increase the viability of nm SiO₂-induced A549 cells and decrease the finer particles-induced cell damages. Militarine is especially promising compound for chemoprevention of lung cancer induced by nm SiO₂ through activation of Nrf2 pathway.

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area

Background

Although the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited.

Method

A total of 68,416 people in South China were enrolled and followed up during 2019-2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM₁₀ and O₃ exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated.

Results

Overall, each 10 μg/m³ increase in PM₁₀ concentration was associated with a 3.1% (95%CI: 1.3%-4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O₃ exposure (6.8%, 5.5%-8.2%). Furthermore, each 10 μg/m³ increase in PM₁₀ was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O₃ was relevant to a 4.7%-22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM₁₀ exposure (P _interaction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O₃ (P _interaction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O₃ exposure (P _interaction: 0.032).

Conclusion

We provide comprehensive evidence on the hospitalization hazard of monthly PM₁₀ and O₃ exposure and their interaction with individual factors.

Reduced inequality in ambient and household PM2.5 exposure in China

The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM_2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM_2.5; however, the total PM_2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM_2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM_2.5 exposure caused 1.13 (0.63-1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM_2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM_2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM_2.5 exposure inequality over time.

Optimized environmental justice calculations for air pollution disparities in Southern California

An Environmental Justice (EJ) analysis was carried out using full Chemical Transport Models (CTMs) over Los Angeles, California, to determine how the combination of domain size and spatial resolution affects predicted air pollution disparities in present day and future simulations when data support from measurements is not available. One set of simulations used the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF/Chem) with spatial resolution ranging from 250 m to 36 km, comparable to census tract sizes, over domains ranging in size from 320 km² to 10,000 km². A second set of simulations used the UCD/CIT CTM with spatial resolution ranging from 4 km to 24 km over domains ranging in size from 98,000 km² to 1,000,000 km². Overall WRF/Chem model accuracy improved approximately 9% as spatial resolution increased from 4 km to 250 m in present-day simulations, with similar results expected for future simulations. Exposure disparity results are consistent with previous findings: the average Non-Hispanic White person in the study domain experiences PM_2.5 mass concentrations 6-14% lower than the average resident, while the average Black and African American person experiences PM_2.5 mass concentrations that are 3-22% higher than the average resident. Predicted exposure disparities were a function of the model configuration. Increasing the spatial resolution finer than approximately 1 km produced diminishing returns because the increased spatial resolution came at the expense of reduced domain size in order to maintain reasonable computational burden. Increasing domain size to capture regional trends, such as wealthier populations living in coastal areas, identified larger exposure disparities but the benefits were limited. CTM configurations that use spatial resolution/domain size of 1 km/10³ km² and 4 km/10⁴ km² over Los Angeles can detect a 0.5 μg m^-3 exposure difference with statistical power greater than 90%. These configurations represent a balanced approach between statistical power, sensitivity across socio-economic groups, and computational burden when predicting current and future air pollution exposure disparities in Los Angeles.

Exposure and Inequality of PM2.5 Pollution to Chinese Population: A Case Study of 31 Provincial Capital Cities from 2000 to 2016

Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m^-3 per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m^-3) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

Does the environmental inequality matter? A literature review

The environmental inequality theory reveals that the risk of environmental pollution exposure varies among regions and groups and that particular groups face a higher threat of environmental pollution. In recent years, studies on the environmental inequality issue in developed countries have been increasing, while related literature on developing countries is very scarce. It has been found that some factors, such as race and economic status, have a close relationship with the risk of environmental pollution exposure faced by individuals. For the first time, this paper provides an extensive review of existing theoretical and empirical studies on environmental inequality. We review, in detail, the evolution of the environmental inequality theory, including the definition and measurement of environmental inequality. Further, we provide a systematic refresher on the main performance of environmental inequality, including health, education, labor productivity, and real estate prices. We also identify several causes of environmental inequality, such as ethnic differences, economic status, human capital, and household registration systems. Finally, we discuss prospects for the future research on this issue.

Short-term effects of indoor and outdoor air pollution on the lung cancer morbidity in Henan Province, Central China

Lung cancer is one of the most common cancer types and a major cause of death. The relationship between lung cancer morbidity and exposure to air pollutants is of particular concern. However, the relationship and difference in lung cancer morbidity between indoor and outdoor air pollution effects remain unclear. In this paper, the aim was to comprehensively investigate the spatial relationships between the lung cancer morbidity and indoor-outdoor air pollution in Henan based on the standard deviation ellipse, spatial autocorrelation analysis and GeoDetector. The results indicated that (1) the spatial distribution of lung cancer morbidity was related to the geomorphology, while high-morbidity areas were concentrated in the plains and basins of Central, Eastern and Southern Henan. (2) Among the selected outdoor air pollutants, PM_2.5, NO₂, SO₂, O₃ and CO were significantly correlated with the lung cancer morbidity. The degree of indoor air pollution was measured by the use of heating energy, and the proportions of coal-heating households, households with coal/biomass stoves and households with heated kangs were highly decisive in regard to the lung cancer morbidity. (3) The interaction between two factors was more notable than a single factor in explaining the lung cancer morbidity. Moreover, the interaction type was mainly nonlinear enhancement, and the proportion of households with coal/biomass stoves imposed the strongest interaction effect on the other factors.

A Social Vulnerability Index for Air Pollution and Its Spatially Varying Relationship to PM2.5 in Uganda

Fine particulate matter (PM2.5) is a ubiquitous air pollutant that is harmful to human health. Social vulnerability indices (SVIs) are calculated to determine where vulnerable populations are located. We developed an SVI for Uganda to identify areas with high vulnerability and exposure to air pollution. The 2014 national census was used to create the SVI. Mean PM2.5 at the subcounty level was estimated using global PM2.5 estimates. The mean PM2.5 for Kampala at the parish level was estimated using low-cost PM2.5 sensors and spatial interpolation. A local indicator of spatial association (LISA) was performed to determine significant spatial clusters of social vulnerability, and a bivariate analysis was performed to identify where significant associations were between SVI and annual PM2.5 mean concentrations. The LISA results showed significant clustering of high SVI in the northern and western regions of the country. The spatial bivariate analysis showed positive linear associations between SVI and PM2.5 concentration in subcounties in the northern, western, and central regions of Uganda, as well as in certain northern parishes in Kampala. Our approach identified areas facing both high social vulnerability and air pollution levels. These areas can be prioritized for health interventions and policy to reduce the impact of ambient PM2.5.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Assessing personal travel exposure to on-road PM2.5 using cellphone positioning data and mobile sensors

PM_2.5 pollution imposes substantial health risks on urban residents. Previous studies mainly focused on assessing peoples' exposures at static locations, such as homes or workplaces. There has been a scarcity of research that quantifies the dynamic PM_2.5 exposures of people when they travel in cities. To address this gap, we use cellphone positioning data and PM_2.5 concentration data collected from smart sensors along roads in Guangzhou, China, to assess personal travel exposure to on-road PM_2.5. First, we extract the trips of cellphone users from their trajectories and use the shortest path algorithm to calculate their travel routes on the road network. Second, the travel exposure of each user is estimated by associating their movement patterns with PM_2.5 concentrations on roads. The result shows that most users' average travel exposures per hour fall within the range of 20 ug/m³ to 75 ug/m³. Travel exposure varies across users, and 54.0% of users experience low travel exposure throughout the day, 25.5% of users experience high travel exposure in the evening, and 20.5% of users experience high travel exposure in the afternoon. Furthermore, the impacts of on-road PM_2.5 on urban populations are uneven across roads. More attention should be given to roads with high PM_2.5 concentrations and traffic flows in each period, such as Huan Shi Middle Road in the morning, Inner Ring Road in the afternoon, and Xinjiao Middle Road in the evening. The findings in this study can contribute to a more in-depth understanding of the relationship between air pollution and the travel activities of urban populations.

Air pollution control efficacy and health impacts: A global observational study from 2000 to 2016

Particulate matter with aerodynamic diameter ≤2.5 μm (PM_2.5) concentrations vary between countries with similar carbon dioxide (CO₂) emissions, which can be partially explained by differences in air pollution control efficacy. However, no indicator of air pollution control efficacy has yet been developed. We aimed to develop such an indicator, and to evaluate its global and temporal distribution and its association with country-level health metrics. A novel indicator, ambient population-weighted average PM_2.5 concentration per unit per capita CO₂ emission (PM_2.5/CO₂), was developed to assess country-specific air pollution control efficacy (abbreviated as APCI). We estimated and mapped the global average distribution of APCI and its changes during 2000-2016 across 196 countries. Pearson correlation coefficients and Generalized Additive Mixed Model (GAMM) were used to evaluate the relationship between APCI and health metrics. APCI varied by country with an inverse association with economic development. APCI showed an almost stable trend globally from 2000 to 2016, with the low-income groups increased and several countries (China, India, Bangladesh) decreased. The Pearson correlation coefficients between APCI and life expectancy at birth (LE), infant-mortality rate (IMR), under-five year of age mortality rate (U5MR) and logarithm of per capita GDP (LPGDP) were -0.57, 0.65, 0.66, -0.59 respectively (all P values < 0.001). APCI could explain international variation of LE, IMR and U5MR. The associations between APCI and LE, IMR, U5MR were independent of per capita GDP and climatic factors. We consider APCI to be a good indicator for air pollution control efficacy given its relation to important population health indicators. Our findings provide a new metric to interpret health inequity across the globe from the point of climate change and air pollution control efficacy.

Who are more exposed to PM2.5 pollution: A mobile phone data approach

BACKGROUND

Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales.

OBJECTIVES

This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales.

METHODS

The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects.

RESULTS

We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons).

CONCLUSIONS

There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

PM_2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM_2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM_2.5 from 2015 to 2018 in the YRDUA. Results showed that PM_2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM_2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM_2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM_2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM_2.5 and CO was the most consistent, and the correlation between PM_2.5 and SO₂/NO₂ improved with the increase of time scale, while the correlation between PM_2.5 and O₃ was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM_2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

A Framework to Classify Environmental Inequity in Absolute and Relative Terms, and Its Application in Beijing

Although reducing environmental inequities is widely recognized as an essential step towards sustainable cities, decision-makers frequently lack the tools to identify inequity distribution patterns and designing effective intervention policies. This study seeks to present a framework that can help decision-making processes by classifying environmental inequity districts in multiple perspectives, especially in absolute and relative terms. This framework includes four steps: (A) variable selection, (B) data normalization, (C) ranking indicators, (D) summarizing inequity classification, which then assign results to selected areas. The framework aims to classify and compare environmental inequities in multiple perspectives, and can be applied in various environmental problems, with advantages such as high acceptability and clear comprehensibility. To show the potential use of this framework, a case application in Beijing, China, was conducted to evaluate the environmental inequity of air pollution. The results suggest that decision-makers should focus on the central urban area and some southern regions of Beijing to implement various improvement policies. Based on the results from Beijing, how the framework can be used to help decision-makers, the future roles of this framework with the government and the public, as well as the framework’s limitations are further discussed.

Spatial Distribution and Formation Mechanism of Water-soluble Inorganic Ions in PM2.5 During a Typical Winter Haze Episode in Guilin, China

A 5-day PM_2.5 sampling campaign was conducted during a typical haze episode from December 16 to 20, 2016, at five urban sites and one background site in Guilin, a famous tourist city in Southern China. A total of 30 PM_2.5 samples were collected, and water-soluble inorganic ions (WSII) (SO₄^2-, NO₃^-, NH₄⁺, Ca²⁺, K^+, Cl^-, Na⁺, and Mg²⁺) were determined using ion chromatography. Correlation analysis, principal component analysis, and coefficient of divergence were applied to identify the formation mechanisms of secondary inorganic ions, potential sources, and spatial distribution of WSII. The average mass concentrations of PM_2.5 at each sampling site were 71.6-127.85 μg m^-3, which were more than the National Ambient Air Quality Standard (GB3095-2012, GradeII (35 μg m^-3)) in China. SO₄^2- NO₃^-, and NH₄⁺ were the major WSII, accounting for 34.43-40.59% of PM_2.5 mass. NO₃^-/SO₄^2- ratio revealed that stationary sources-induced PM_2.5 was still remarkable. Cl^-/Na⁺ ratio and their strong correlation (r = 0.824) indicated that atmospheric transport from outside urban region played an effective role during the haze episode. Spatial variations of WSII are not pronounced at five urban sites except the background site. High relative humidity and O₃ contributed to evidently influence the transformation of SO₂ to SO₄^2- but not obvious to NO_x oxidation. Finally, the major sources of WSII are identified as the mixture of sea salt, coal combustion, biomass burning, vehicle exhaust and agricultural emissions (66.892%), and fugitive sources (19.7%).

Airborne bacterial communities and antibiotic resistance gene dynamics in PM2.5 during rainfall

The biotoxicity and public health effects of airborne bacteria and antibiotic resistance genes (ARGs) in fine particulate matter (PM_2.5) are being increasingly recognized. The characteristics of bacterial community composition and ARGs in PM_2.5 under different rainfall conditions were studied based on the on-site synchronous measurements in downtown Beijing. Marked differences were evident in the bacterial community characteristics of PM_2.5 before, during, and after rain events (p < 0.05). The rain intensities affected the bacterial community abundance in PM_2.5 and heavy rain had greater washing effects. The Proteobacteria (phylum level), α-Proteobacteria (class level), Pseudomonadales (order level), Pseudomonadaceae (family level), and Cyanobacteria (genus level) were the dominant bacterial taxa associated with PM_2.5 in Beijing during rain events. However, the bacteria at each level that displayed the biggest percentage variance was not the dominant type under different rain intensities. The ermB, tetW, and mphE genes were the primary ARGs, with abundances of 18 to 30 copies/m³, which was a relatively smaller value than other observations. Real-time monitoring of the meteorological condition of rain events and physicochemical properties of PM_2.5 were used to identify the main factors during rainfall. The bacterial community was sensitive to the ionic and metal element components of PM_2.5 during rainfall. The abundance of ARGs was closely correlated with some groups of the bacterial community, which were also close to the initial value before the rain. Statistical analysis demonstrated that temperature, relative humidity, and duration of rain were the primary meteorological factors for the biological characteristics. The ionic species, rather than metal elements, in PM_2.5 were the sensitive factors for the bacteria community and ARGs, which varied at the phylum, class, order, family, and genus levels. The observations provide insights for the biological risk assessment in an urban rainfall water and the potential health impact on citizens.

Comparison of methods for addressing the point-to-area data transformation to make data suitable for environmental, health and socio-economic studies

Soil lead (Pb) provides an important exposure pathway to the human body through soil ingestion and dust inhalation and is closely associated with human health as well as social behaviour. The challenge of transforming different spatial supports arises when linking point data (Pb concentration) to areal data (health status or social behaviour). A detailed review of methodologies for integrating point and areal data has been carried out. Among a number of methodologies, eight methods: (1) average, (2) median, (3) centroids inverse distance weighted (IDW), (4) average block IDW, (5) median block IDW, (6) centroids ordinary kriging (OK), (7) average block OK and (8) median block OK, have been compared using Pb data set in the Greater London Authority (GLA) area. The results indicated that the method of median block IDW was recommended for further investigation of the relationship between Pb concentration and socio-economic factors in the ward-level of the GLA area. The reasons were (i) spatial interpolations were useful for predicting unobserved values when simple average and median could not work in the locations where there were no samples collected in some areal units; (ii) the median value was more suitable than the average value for a skewed data set; (iii) the block method reduced estimation error and provided more representative values of areal units than the centroid method; (iv) IDW reserved more spatial variation than OK, containing more local maxima (hotspot) and local minima. Despite that it is still hard to decide the optimal method, this study has highlighted the point-to-area transformation issue and provided valuable examples to compare the different methods.

Ecological and human health risk assessment

The literature review presented in this paper covers the risk assessment process that is important to human health as well as the health of ecology in the form of receptors. One of the important objectives of present review is to provide summary of the scientific studies published in the year 2018. The review starts with literature published on the assessment of health risks, which are valuable to human and ecology. Most of the literature in the entire article focuses on techniques used for the analysis of scientific data and methods. In addition, review also highlights data interpretation, uncertainty, policy, and regulatory guidance associated with the management of human and ecological risks. Particularly, the review on the risk assessment related to human health and ecology is divided into two main sections. These sections provide broad state of knowledge on the risk assessment process used to health of human and ecological systems focused on investigation of polluted sites, techniques of remediation, and tools required for natural resource management.

Characteristics and secondary formation of water-soluble organic acids in PM1, PM2.5 and PM10 in Beijing during haze episodes

Water-soluble organic acids are widely involved in various atmospheric physicochemical processes and appear as an important fraction of atmospheric aerosols. Nineteen water-soluble organic acids in 12-h PM₁, PM_2.5 and PM₁₀ samples collected in urban Beijing during haze episodes in winter and spring of 2017 were identified to investigate their characteristics and secondary formation mechanism. The molecular distributions of water-soluble organic acids as well as the high ratio of phthalic acid (Ph)/azelaic acid (C₉) indicated severe aromatic secondary organic aerosol pollution during the haze episodes, especially in winter. The diurnal patterns, size distributions, and concentration ratios of specific organic acids were investigated to reveal the pollution characteristics and possible sources of major organic acids in particulate matter in Beijing during haze events. Multiple linear regression was used to tentatively quantify the relative contributions of photochemical oxidation and aqueous-phase oxidation to the formation of total water-soluble organic acids in PM₁, PM_2.5 and PM₁₀ during haze episodes. The formation mechanism of sulfate and nitrate was also investigated for comparison. Different from the secondary formation of sulfate, the secondary formation of water-soluble organic acids showed enhanced contribution of gas-phase photochemical oxidation though the aqueous-phase oxidation was the dominant process. CAPSULE: Molecular analyses of organic acids in PM₁, PM_2.5 and PM₁₀ in Beijing during haze periods revealed their pollution characteristics, possible sources and formation mechanism.