Heavy Industrial City Research Articles

Spatialization and Analysis of China’s GDP Based on NPP/VIIRS Data from 2013 to 2023

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China’s GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development.

The effect of air pollutants on COPD-hospitalized patients in Lanzhou, China (2015-2019).

Lanzhou is the largest heavy industrial city in northwest China and it is a typical geographical valley-like city. However, there are few studies on the relationship between air pollutants and COPD, and their respective sample sizes are small, resulting in inconsistent results. The aim of this study is to analyze the effects of air pollutants on COPD hospitalizations in Lanzhou, China. An ecological time series study with distributed lag non-linear model (DLNM) was used for analysis. Daily COPD hospitalization data in Lanzhou from 1 January 2015 to 31 December 2019 were collected from 25 hospitals, as well as air pollutant data and meteorological data. A total of 18,275 COPD hospitalizations were enrolled. For 10 μg/m3 increase in PM2.5, PM10, SO2, NO2, and 1 mg/m3 increase in CO at lag 07 day, the RR95%CI of COPD hospitalizations were 1.048 (1.030, 1.067), 1.008 (1.004, 1.013), 1.091 (1.048, 1.135), 1.043 (1.018, 1.068), and 1.160 (1.084, 1.242), respectively. The exposure-response curves between air pollutants (except O3-8h) and COPD hospitalizations were approximately linear with no thresholds. Female, and the harmful effect of PM on aged <65 years, the effect of gaseous pollutant on those aged ≥65 years, were stronger, particularly in the cold season. Exposure to air pollutants (except O3-8h) might increase the risk of COPD hospitalizations. O3-8h has a weak and unstable effect on COPD. Exposure to air pollutants (except O3-8h) increases the risk of COPD hospitalizations. O3-8h has a weak and unstable effect on COPD hospital admissions. The harmful effect of gaseous pollutants (except O3-8h) on COPD-hospitalized patients was stronger than that of PM.

Traceability and policy suggestions for ozone pollution in heavy industrial city in Northeast China.

In the heavy industrial city of Northeast China, there has been a significant decrease in particulate matter pollution while experiencing a sharp increase in ozone (O3) pollution. However, the main influencing factors and source contributions to O3 remain unclear. Taking the case of Siping as an example, this study analyzed the spatiotemporal characteristics, assessed local source contributions to O3, and revealed regional transmission effects using numeric simulation and statistical methods. Temporally, higher O3 concentrations were observed in summer and the afternoon, with hourly peaks up to 254µg/m3. Spatially, O3 pollution was mainly contributed by background concentrations (34.52%), external transport (34.50%), and local emissions (30.98%) during the case study period (June 11-18, 2021). Among the local emission sources, biological emissions, the industrial sector, and the traffic sector accounted for 35.30%, 32.09%, and 23.58% of the O3 concentration, respectively. For regional atmospheric transmission, high O3 pollution was accompanied by wind from the southwest directions, and the trajectory of air mass transport suggests that eastern Mongolia, the Korean Peninsula, and its neighboring regions contribute to O3 pollution. Furthermore, sensitivity analysis showed that O3 pollution in Siping is a co-controlled region by anthropogenic volatile organic compounds (AVOCs) and NOX, which implies control in an optimal ratio of VOCs and NOX emissions. Thus, our results highlight the importance of joint prevention and control of O3 pollution in the region, optimization of biogenic landscape ecology, and control of VOCs and NOx in both the industrial and transport sectors.

Chinese cities show different trend toward carbon peak

Understanding the disparities in carbon emission trend among cities is critical for achieving carbon peak goal. However, the status and trends of carbon peaking and reduction in various city types are still unclear. Therefore, this study classified 315 Chinese cities according to their economic and industrial structure by SOM-K-means, aiming to evaluate the trends and dynamic drivers of carbon peaking progress in different city types. The findings reveal a decline in carbon emissions in 110 cities (34.9 %) since 2020. Notably, all city types show potential for carbon reduction and achieving carbon peaking. Specifically, resource-based cities and high-end service cities have the most effect on reducing emissions, with 48.4 % and 42.1 % of the cities declining in carbon emissions. Energy-based and heavy industrial cities face heightened pressure to reduce carbon emissions. Additionally, in high-end service cities, energy efficiency and investment intensity contribute to emission reduction, while industrial structure adjustment decrease carbon emissions in resource-based cities. Furthermore, enhancing energy efficiency effects and R&D intensity are effective ways to significantly reduce carbon emissions in heavy industrial cities. We conclude that differentiating carbon reduction pathways for different cities should constitute be a breakthrough in achieving the goal of carbon peaking. These insights provide recommendations for cities that have yet to reach their carbon peak for both China and other developing countries.

The Decline and Revival of Traditional Heavy Industrial Cities: The Examples of Detroit in the United States and Shiyan in China

The global economic transformation has led to the gradual decline of many traditional heavy industrial cities, which makes how to revitalize these cities an important issue. This paper takes Shiyan and Detroit as case studies on the decline and revitalization of heavy industrial cities. The research in this paper concludes that the main reason for the decline of industrial cities is stagnant economic development. Both Detroit and Shiyan used to rely on the automobile industry as their mainstay, but this mono-industrial structure made the cities' economies excessively dependent on their industries. Economic stagnation has led Detroit and Shiyan to face declining incomes and lower tax revenues at different times, especially under the impact of the economic crisis. Promoting the economic revival of traditional heavy-industry cities can be done through industrial upgrading, innovation-driven and green transformation. At the same time, the city should also increase the cultivation and introduction of talent, build a talent highland and an innovation highland, and provide strong talent support for industrial transformation and development. In conclusion, the revitalization of traditional heavy industrial cities requires comprehensive economic, cultural, environmental, talent, and other considerations, the research in this paper can further explore the development paths and modes of industrial cities, and contribute to the revival and prosperity of industrial cities.

Spatiotemporal variations in soil pollution by polycyclic aromatic hydrocarbons over a 20-year economic boom in different districts of a heavy industrial city in North China

Urbanization-related human activities, such as population aggregation, rapid industrial expansion, and intensified traffic, are key factors that impact local polycyclic aromatic hydrocarbon emissions and their associated health risks. Consequently, regions with varying degrees of urbanization within a megacity may exhibit diverse spatiotemporal patterns in the presence and distribution of soil polycyclic aromatic hydrocarbons, resulting in different levels of ecological risks for local inhabitants following the same period of development. In this study, we measured the concentrations of 16 polycyclic aromatic hydrocarbons in soil samples collected from industrial district and rural district in Tianjin (China) in 2018, and compared with polycyclic aromatic hydrocarbon data in 2001 from a previous study to characterize these regional variations in occurrence, source, and human risk of polycyclic aromatic hydrocarbons induced by urbanization with time and space. The results indicate the 20-year rapid urbanization and industrialization has differentially affected the composition, distribution and sources of polycyclic aromatic hydrocarbons in soils from different economic functional zones in Tianjin. Additionally, its impact on health risks in rural district appeared to be more significant than that in industrial district.

Industrial characteristics and drivers of urban carbon cycle: From an analysis of typical cities in China

The urban carbon cycle is an important part of the global carbon cycle. Clarifying the processes and drivers of the urban carbon cycle is essential for achieving urban carbon neutrality. Research on the urban carbon cycle under various industrial characteristics is still insufficient. In this study, four typical Chinese cities are selected and their carbon cycle processes and influencing factors are analysed. Here we propose an urban carbon neutral rate to evaluate the balance between urban carbon input and carbon output. Through Grey correlation analysis, we explore the main influencing factors of urban carbon neutral rate. The results show that the carbon neutral rate of Chinese cities is uneven and polarised. Heavy industrial cities face major challenges in urban carbon neutrality. In addition, the main factors affecting the carbon neutral rate of cities of different industrial types are not the same. The formulation of urban carbon neutral policies and measures should be differentiated and focused according to the industrial characteristics of cities.

The influence of PM2.5 exposure duration and concentration on outpatient visits of urban hospital in a typical heavy industrial city.

To explain the duration and dose effects of pollutant exposure on public health and provide scientific data for air pollution prevention and control and disease prevention by examining the influence of PM2.5 concentration and exposure duration on daily outpatient visits among patients with cardiovascular, cerebrovascular, and respiratory diseases in a typical heavy industrial city in China. Daily outpatient data on cardiovascular, cerebrovascular, and respiratory diseases and regional PM2.5 exposure duration and concentration were collected from a provincial hospital in Taiyuan, China, from 2016 to 2021. The correlations of numeric variables were analyzed using the Pearson correlation method. A generalized additive model (GAMs) was also established to investigate the effects of PM2.5 concentration and exposure duration on outpatient visits. Correlation analysis showed that the outpatient visits in Taiyuan was significantly correlated with the PM2.5 concentration and exposure duration. The longer the exposure time of PM2.5 pollution, the stronger the correlation of PM2.5 with outpatient visits showed. Cardiovascular outpatient visits were extremely significant related with medium to long-term exposure of PM2.5 (exposure with more than 30days) (p < 0.001). In addition, outpatient visits of cerebrovascular and respiratory disease were extremely significant correlated with PM2.5 (exposures within 0-360days) (p < 0.001). The results of GAMs showed the linear or the nonlinear relationships between outpatient visits and exposure of PM2.5. Among the linear relationships, when average concentration of PM2.5 (exposure within less than 15days) increased by 1mg/m3, the cardiovascular outpatient visits increased most dramatically (by about 440 people). For nonlinear relationships, when the average PM2.5 concentration (exposure with over 30days or more) increased by 1mg/m3, the most dramatic increase occurred in cardiovascular outpatient visits (with a maximum increase of 7000), followed by cerebrovascular outpatient visits (with a maximum increase of 1200), and respiratory outpatient visits (with a maximum increase of 250). The GAMs also revealed a dose effect in the relationship between outpatient visits and PM2.5 exposure. In moderately polluted air (based on air quality standards of China, GB3095-2012), when the average concentration of PM2.5 increased by 1mg/m3, the cardiovascular outpatient visits increased the most (by 1200 people), followed by cerebrovascular outpatient visits (by 200 people) and respiratory outpatient visits (by 20 people). We concluded that outpatient visits in cardiovascular, cerebrovascular, and respiratory disease are closely correlated with the concentration and exposure duration of air pollution. There is a linear relationship between short-term air pollution exposure (exposure within less than 15days) and outpatient visits. As PM2.5 concentration increases, cardiovascular outpatient visits increase gradually, with its growth trend exceeding that of cerebrovascular and respiratory disease. There is a nonlinear relationship between medium and long-term air pollution exposure (exposure with more than 30days) and outpatient visits, with cardiovascular and cerebrovascular outpatient visits showed a nonlinear but overall upward trend when the atmosphere is moderately polluted.

Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt

Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China.

CEDUP: Using incremental learning modeling to explore Spatio-temporal carbon emission distribution and unearthed patterns at the municipal level

Carbon emissions reduction has become a world consensus. Cities have an essential role to play in addressing emission reductions. However, previous studies have estimated China's municipal-level carbon emissions based on provincial-level emission patterns, and such a top-down carbon emission accounting approach has led to biased results. Therefore, this study employed an incremental learning ensemble model and a Savitzky-Golay algorithm tomeasure carbon emission distribution and unearth patterns at the municipal level based on nighttime light (NTL) and regional development characteristics (GDP, population, patents, industry structure). The performance of the proposed method is substantially better than its counterparts in terms of municipal-level estimation (R-square boosted by 20.64%). This research shows significant difference in carbon emission mechanisms between provinces and cities and demonstrates that carbon emissions are time-continuous. It also shows that per-capita carbon emissions are peaking in many China's cities, except in some heavy industrial cities. Our approach provides accurate and dynamic monitoring of municipal emissions in China.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources.

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO2, NO2, CO, O3, PM2.5, and PM10 were 8.61-13.76μgm-3, 26.53-36.06μgm-3, 0.79-1.31mgm-3, 82.24-87.62μgm-3, 37.98-51.10μgm-3, and 84.15-97.47μgm-3. The concentrations of air pollutants (except O3) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM10 in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Response of Ecosystem Service Value to Spatio-Temporal Pattern Evolution of Land Use in Typical Heavy Industry Cities: A Case Study of Taiyuan City, China

Ecosystem services value (ESV) has been one index of quantitative evaluation for the ecological livability of heavy industry cities in the new era, which is intimately relevant to patterns of spatio-temporal changes in land use. This study aims to reveal the response of ecosystem service value in heavy industrial cities to the spatial-temporal evolution structure of land use and to analyze the cold and hot spots and sensitivity. In this study, Taiyuan was taken as an example, and Landsat images were adopted as the basic data. This study used intensity analysis, revised ESV, fishing nets, sensitivity analysis, and the methods of hotspot analysis and spatial overlay. The results showed as follows, (1) The characteristics of land use structure evolution mainly focus on the increase of construction land in the early and the rapid development stage of heavy industry cities. All land use types were partly transferred to construction land, but farmland was the main source, with the largest change intensity in the rapid development stage in Taiyuan; (2) The low-value zones of ESV were mostly distributed in the main urban area for construction and farmland, while the high-value zones were primarily distributed in the forestland and grassland. They were distributed in the Fenhe River valley, western and northern mountainous and hilly areas of Taiyuan. The total ESV continued to decline from 2003 to 2018, with a loss amount of RMB 29 million; (3) The patches of land use change were more and more broken, and the spatial distribution of the cold and hot spots was more and more dispersed. The cold and hot spots of ESV were concentrated in the eastern main urban area and its surrounding areas and expanded to the north and south; (4) The forestland was the most sensitive land factor of ESV. The study provides a theoretical method for land use planning, environmental governance, and ecological restoration in heavy industry cities in the new era.

Multi-element features and trace metal sources of road sediment from a mega heavy industrial city in North China

As the primary carrier of harmful elements, road sediment poses severe hazards to human health and ecological environment, especially in megacities. Based on the industrial cities in North China, this research focused on the multi-element features and the pollution levels, sources, and spatial distributions of trace metals in road sediment of Shijiazhuang. The mean levels of P (928.4 mg kg−1), S (1446.2 mg kg−1), Cl (783.9 mg kg−1), Br (5.3 mg kg−1), Na2O (2.0%), CaO (9.9%), Co (36.0 mg kg−1), Pb (38.0 mg kg−1), Cu (34.7 mg g−1), Zn (149.1 mg kg−1), Ba (518.1 mg kg−1), and Sr (224.9 mg kg−1) in road sediment were greater than their soil background values. Trace metals in most samples was moderately (75%) and heavily contaminated (15.6%). The industrial areas, congested roads, and residential areas in the northeast, middle and south of Shijiazhuang are the hotspots of trace metals pollution. A comprehensive analysis of trace metals sources indicated that Ni, V, Ga, Rb, Y, Sc, La, Ce, Zr, and Hf were mainly from natural source, which contributed to 34.2% of the total trace metals concentrations. Cu, Pb, Zn, Cr, Ba, Sr, and Mn primarily originated from mixed source, which accounted for 46.5%. Co principally came from building source, which accounted for 19.3%. This study shows that industrial discharges, construction dust and traffic emissions are the primary anthropogenic sources of trace metals in road sediment in the study area.

Environmentally persistent free radicals in PM2.5 from a typical Chinese industrial city during COVID-19 lockdown: The unexpected contamination level variation

The outbreak of COVID-19 has caused concerns globally. To reduce the rapid transmission of the virus, strict city lockdown measures were conducted in different regions. China is the country that takes the earliest home-based quarantine for people. Although normal industrial and social activities were suspended, the spread of virus was efficiently controlled. Simultaneously, another merit of the city lockdown measure was noticed, which is the improvement of the air quality. Contamination levels of multiple atmospheric pollutants were decreased. However, in this work, 24 and 14 air fine particulate matter (PM2.5) samples were continuously collected before and during COVID-19 city lockdown in Linfen (a typical heavy industrial city in China), and intriguingly, the unreduced concentration was found for environmentally persistent free radicals (EPFRs) in PM2.5 after normal life suspension. The primary non-stopped coal combustion source and secondary Cu-related atmospheric reaction may have impacts on this phenomenon. The cigarette-based assessment model also indicated possible exposure risks of PM2.5-bound EPFRs during lockdown of Linfen. This study revealed not all the contaminants in the atmosphere had an apparent concentration decrease during city lockdown, suggesting the pollutants with complicated sources and formation mechanisms, like EPFRs in PM2.5, still should not be ignored.

The removal of atmospheric aerosols in a heavy industrial coastal city in China with frequent rainfall

Rapid urbanization and heavy industrialization generally result in serious aerosol pollution. Contrary to this conventional wisdom, Zhanjiang, one industrial city in the southernmost point of the Chinese mainland, is not accompanied by aerosol pollution and its air quality index always ranks high compared to other cities in China. To investigate this contradiction, 72-hour total suspended particles (TSPs) and water-soluble inorganic ions (WSIIs; including Mg2+, Ca2+, K+, Na+, NH4+, Cl-, NO3-, and SO42-) were collected in Zhanjiang, China, from November 2018 to November 2019. The relative humidity (RH) was higher than 80% throughout the whole year in Zhanjiang. However, the TSPs and WSIIs were not correlated with RH, indicating that RH can increase the particle size, but this had a minor impact on the dry deposition rate. The larger particles induced by RH were easily captured by wet precipitation, leading to a seasonal pattern with higher TSP and WSII mass concentrations during the dry and cool season and lower values during the hot and rainy season. This seasonal pattern and high aerosol acidity indicate that TSPs, WSIIs, and acidic gaseous precursors from the local sources were preferentially scavenged by the abundant rainfall and high precipitation frequency. Principal component analysis (PCA) results suggest that relatively clean marine emissions and secondary aerosols were the most important sources of TSPs and WSIIs. Our results indicate that the inconsistency between the heavy industrial activities and excellent air quality in Zhanjiang may be related to the high precipitation frequency (63%) and the marine dilution effect (27%).

Lead and stable lead isotopes as tracers of soil pollution and human health risk assessment in former industrial cities of Hungary

Lead is a toxic heavy metal that has become much more prevalent in the environment since industrialization and causes considerable health problems. The current study investigated the spatial distribution and sources of lead from urban soils in former heavy industrial cities, Salgótarján and Ózd (northeastern Hungary). Even today, industrial byproduct (e.g., coal ash, smelter slag) dumps in both cities pose a real threat to the residents. Our analytical results acquired on samples from kindergartens, playgrounds, parks, roadside, etc. indicated a heterogeneous lead distribution with 8.5–1692 mg kg-1 for Salgótarján and 6.6–1674 mg kg-1 for Ózd. The enrichment of Pb results from the high variability of the potential anthropogenic contamination sources, such as iron and steel work, coal mines, coal-fired power plant, smelter slag, vehicle emission, etc., in the studied areas. The potential lead emission sources were defined by 206Pb/207Pb – 208Pb/207Pb and 206Pb/204Pb – 208Pb/204Pb isotopic ratios in urban soil samples and the local endmembers: namely brown forest soil (206Pb/207Pb: 1.20 in Salgótarján and 1.21 in Ózd), brown coal (206Pb/207Pb: 1.18 in Salgótarján and 1.26 in Ózd), and industrial byproducts (206Pb/207Pb: 1.18 coal ash in Salgótarján and 1.12–1.16 smelter slags in Ózd). A positive correlation between TOC, TN, Mn, and Pb in Salgótarján and between Fe, Mn, and Pb in Ózd, shows that the urban soil characteristics play a significant role in Pb distribution in the sampling sites. Our study confirms that coal ash in Salgótarján and smelter slag in Ózd can be considered primary anthropogenic Pb contamination sources, resulting in low chronic health risks for residents.

Characteristics, Sources, and SOAP of VOCs During Winter in Jiyuan

Haze pollution events often occur in the heavy industry city of Jiyuan. Volatile organic compounds (VOCs) are the precursors of secondary organic aerosol (SOA), which accounts for 15%-20% of particulate matter (PM2.5). Here, PM2.5, O3, VOCs, and trace gases were monitored by using online instruments in Jiyuan from December 1st to 31st. The characteristics, sources, and secondary organic aerosol potential (SOAP) of VOCs were analyzed. The mean concentrations of TVOC were (54.3±27.5)×10-9. Alkanes, halocarbons, and alkynes were the predominant VOCs. The positive matrix factorization model was used to identify and apportion VOCs sources. Eight major sources of VOCs were identified, which included liquefied petroleum gas (LPG) or natural gas (NG), the polyvinyl chloride (PVC) industry, vehicular exhaust, the coking industry, solvent usage, industry, technological process, and fuel evaporation. The SOAP of aromatics was the largest. Among them, BTEXs were the dominant contributors to SOAP.

Bioavailability and health risk of toxic heavy metals (As, Hg, Pb and Cd) in urban soils: A Monte Carlo simulation approach

Toxic heavy metals pollution in urban soil has become a major global issue due to its adverse effects on the environment and human health. In this paper, 26 soil samples were analyzed to assess the speciation, bioavailability and human health risk of Arsenic (As), Mercury (Hg), Lead (Pb) and Cadmium (Cd) in urban soils of a heavy industrial city in NE China by using a Monte Carlo simulation approach. The results showed that As, Hg, Pb and Cd concentrations in the soil all exceed the corresponding background value of study area. Mercury displays the highest value of geo-accumulation index (Igeo), followed by Cd, Pb and As. The pollution load index (PLI) value (>2) indicates a moderate pollution level in the study area. The chemical speciation of HMs mainly exists in residual fraction except Cd. The probabilistic health risk assessment demonstrated that the mean values of Total Carcinogenic Risk (TCR) and Hazard Index (HI) calculated with total concentration are at the unacceptable level, with a higher risk to children than adults. However, the mean values calculated with bioavailable fraction are all within the acceptable level. The mean value of TCR and HI obtained by bioavailable fraction is about 96% and 95% lower than that obtained by total concentration, respectively. Thus, this study suggested that the bioavailable fraction of HMs is a more reliable parameter for health risk assessment, while the total concentration of HMs can overestimate the true risk. The results of this study provide some insight into the speciation, bioavailability and health risks of toxic heavy metals in urban soils in those heavy industrial cities.

Extrapolation of anthropogenic disturbances on hazard elements in PM2.5 in a typical heavy industrial city in northwest China.

Baoji is a typical heavy industrial city in northwest China. Its air quality is greatly impacted by the emission from the factories. Elements in fine particulate matter (PM2.5) that are greatly emitted from anthropogenic sources could pose diverse health impacts on humans. In this study, an online AMMS-100 atmospheric heavy metal analyzer was used to quantify 30 elements in PM2.5 under the weak and strong anthropogenic disturbance scenarios before the city lockdown period (from January 9th to 23rd) and the lockdown period (from January 26th to February 9th) due to the outbreak of COVID-19 in 2020. During the lockdown period, the average total concentration of total quantified elements was 3475.0 ng/m3, which was 28% and 33% lower than that of the week and strong anthropogenic disturbance scenarios during the pre-lockdown period. The greatest reductions were found for the elements of chromium (Cr), titanium (Ti), manganese (Mn), and Zinc (Zn), consistent with the industrial structure of Baoji. The mass concentrations of most elements showed obvious reductions when the government post-alerted the industries to reduce the operations and production. Dust, traffic sources, combustion, non-ferrous metal processing, and Ti-related industrial processing that are the contributors of the elements in the pre-lockdown period were apportioned by the positive matrix factorization (PMF) model. Substantial changes in the quantified elements’ compositions and sources were found in the lockdown period. Health assessment was conducted and characterized by apportioned sources. The highest non-carcinogenic risk (HQ) was seen for Zn, demonstrating the high emissions from the related industrial activities. The concentration level of arsenic (As) exceeded the incremental lifetime carcinogenic risk (ILCR) in the lockdown period. This could be attributed to the traditional firework activities for the celebration of the Chinese New Year within the lockdown period.

Heavy metal ion concentration in the amniotic fluid of preterm and term pregnancies from two cities with different industrial output.

The growth and development of the fetus is a complex phenomenon that can be influenced by several variables. High quantities of heavy metal ions in the amniotic fluid have been linked to poor health, especially in industrial, polluted and poor areas. The aim of the present study was to assess the differences in the concentration of these ions between preterm (weeks 15-37) and term pregnancies (starting at week 37). Another objective was to compare pregnancies from two cities with different industry levels. Two sample lots from two Romanian cities were analyzed. A total of 100 patients from Timisoara were compared with 60 from Petrosani, a heavy industry city in Romania. Demographic data were collected, and amniocentesis was performed on all women. Lead (Pb), copper (Cu), nickel (Ni), cadmium (Cd), arsenic (As), iron (Fe) and zinc (Zn) concentrations were assessed. Descriptive and analytical statistics were performed using the Mann-Whitney U test for non-parametric data and the Fisher's exact test for categorical data. In addition, categorical data was represented graphically. In the Timisoara cohort, the differences in heavy metal concentrations between preterm and term pregnancies were not statistically significant. In the Petrosani cohort, however, the concentrations of Zn (P=0.02606) and Cd (P=0.01512) were higher in preterm than in term pregnancies. When comparing the two cohorts as a whole, the concentration of Pb (P=0.04513), Cd (P=0.00002), As (P=0.03027) and Zn (P<0.00001) were higher in the patients from Petrosani than in those from Timisoara. Only Cu concentrations were higher in the Timisoara cohort (P<0.00001). The concentrations of Ni (P=0.78150) and Fe (P=0.44540) did not differ statistically. Thus, amniocentesis is an important diagnostic and exploratory tool in determining differences in the concentrations of elements such as heavy metal ions. Research over a longer period of time should be carried out to examine the relation between heavy metal ions concentration and possible postnatal health outcomes.