Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China

Liu, Huanjia; Tian, Hezhong; Zhang, Kai; Liu, Shuhan; Cheng, Ke; Yin, Shasha; Liu, Yongli; Li, Xiangyang; Wu, Yiming; Liu, Wei; Bai, Xiaoxuan; Wang, Yong; Shao, Panyang; Luo, Lining; Lin, Shu‐Min; Chen, Jing; Liu, Xingang

doi:10.1016/j.scitotenv.2018.12.068

Cited by 71 publications

(26 citation statements)

References 70 publications

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“…Some diagnostic statistical analyses have been carried out, In order to investigate any spatial differences in PM 10 concentration, in its metals content, and further differences between holidays and working days, or other temporal variations, the COD has been calculated. This coefficient is often used when there is the necessity of investigating the discrepancy between two different datasets, which may be, for example, two sampling sites, two different events, two seasons, years of sampling, as well as variables, like chemical concentrations (Liu et al, 2019;Ramírez et al, 2019;Galindo et al, 2020).…”

Section: Coefficient Of Divergence (Cod)mentioning

confidence: 99%

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Morabito

Gregoris

Belosi

et al. 2020

Front. Environ. Sci.

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This work presents and discusses the results of multi-year measurements of air toxics concentrations from different sites of Venice Lagoon. The aim of the study is the characterization of the air quality of the area, in terms of PM 10 , PM 10-bound metals, and polycyclic aromatic hydrocarbons (PAHs) concentrations, even with the individuation of the related inhalation risk and the identification of the main contaminants' sources. The study moreover provides an important multi-years trend of chemical characterization of the Venice Lagoon. Sampling for PM 10 and its metal content was carried out between 2010 and 2015, using low volume sequential aerosol samplers; V, Cr, Fe, Co, Ni, Cu, Zn, As, Mo, Cd, Sb, Tl, and Pb were measured by ICP-MS. Samples for PAHs concentration in gas and particle phases were collected from 2010 to 2014, using a high-volume air sampler that allows to sample both phases simultaneously. Samples for PAHs determinations were analyzed by GC-HRMS. Concentrations of air toxics have been studied with the use of enrichment factor, the coefficient of divergence, and the Mann-Kendal test, to individuate eventual local divergences, seasonal and other temporal trends. The inhalation risk assessment has been achieved by the calculation of the cumulative incremental carcinogenic risk for all the air toxics analyzed. The sources' identification and impact evaluation have been carried out using the atmospheric vanadium concentration, to calculate the primary contribution of ship traffic to PM 10 , the Positive Matrix Factorization, and the Diagnostic Ratios, with the aim of evaluating the impact of construction activities of M.o.S.E, (Modulo Sperimentale Elettromeccanico-Electromechanical Experimental Module) as well as of ship traffic and other possible air toxics' sources. Temporal trends indicate a slight decrease of PAHs and PM 10 and of its content of Pb, Zn, Sb. Sharp increases of As and Cd during 2014 and 2015 may be due to local sources like emissions from the construction yard, ship, and road traffic. The cumulative incremental carcinogenic risk was below the unconditionally acceptable risk; Co and As are the most important contributors among metals, followed by Cd and Ni while the PAH congeners that most contribute to the carcinogenic risk were benzo(a)pyrene and dibenzo(A,H)anthracene.

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Section: Coefficient Of Divergence (Cod)mentioning

confidence: 99%

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Morabito

Gregoris

Belosi

et al. 2020

Front. Environ. Sci.

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“…The trend of the change of summer low, winter high is consistent with the annual monitoring results of atmospheric particles. Poor diffusion conditions in winter and high pollutant emissions in the heating season are the reasons for the high concentration of PM in winter [ 23 , 24 , 25 , 26 ], while meteorological factors in summer (large precipitation, high temperature, strong radiation forcing and high atmospheric boundary layer, the atmospheric vertical diffusion is the most favorable) are the important factors for the low concentration of PM [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

Effect of Different Pollution Parameters and Chemical Components of PM2.5 on Health of Residents of Xinxiang City, China

Wang

Kaur

et al. 2021

IJERPH

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The present study was planned to explore the pollution characteristics, health risks, and influence of atmospheric fine particulate matter (PM2.5) and its components on blood routine parameters in a typical industrial city (Xinxiang City) in China. In this study, 102 effective samples 28 (April–May), 19 (July–August), 27 (September–October), 28 (December–January) of PM2.5 were collected during different seasons from 2017 to 2018. The water-soluble ions and metal elements in PM2.5 were analyzed via ion chromatography and inductively coupled plasma–mass spectrometry. The blood routine physical examination parameters under different polluted weather conditions from January to December 2017 and 2018, the corresponding PM2.5 concentration, temperature, and relative humidity during the same period were collected from Second People’s Hospital of Xinxiang during 2017–2018. Risk assessment was carried out using the generalized additive time series model (GAM). It was used to analyze the influence of PM2.5 concentration and its components on blood routine indicators of the physical examination population. The “mgcv” package in R.3.5.3 statistical software was used for modeling and analysis and used to perform nonparametric smoothing on meteorological indicators such as temperature and humidity. When Akaike’s information criterion (AIC) value is the smallest, the goodness of fit of the model is the highest. Additionally, the US EPA exposure model was used to evaluate the health risks caused by different heavy metals in PM2.5 to the human body through the respiratory pathway, including carcinogenic risk and non-carcinogenic risk. The result showed that the air particulate matter and its chemical components in Xinxiang City were higher in winter as compared to other seasons with an overall trend of winter > spring > autumn > summer. The content of nitrate (NO3−) and sulfate (SO42−) ions in the atmosphere were higher in winter, which, together with ammonium, constitute the main components of water-soluble ions in PM2.5 in Xinxiang City. Source analysis reported that mobile pollution sources (coal combustion emissions, automobile exhaust emissions, and industrial emissions) in Xinxiang City during the winter season contributed more to atmospheric pollution as compared to fixed sources. The results of the risk assessment showed that the non-carcinogenic health risk of heavy metals in fine particulate matter is acceptable to the human body, while among the carcinogenic elements, the order of lifetime carcinogenic risk is arsenic (As) > chromium(Cr) > cadmium (Cd) > cobalt(Co) > nickel (Ni). During periods of haze pollution, the exposure concentration of PM2.5 has a certain lag effect on blood routine parameters. On the day when haze pollution occurs, when the daily average concentration of PM2.5 rises by 10 μg·m−3, hemoglobin (HGB) and platelet count (PLT) increase, respectively, by 9.923% (95% CI, 8.741–11.264) and 0.068% (95% CI, 0.067–0.069). GAM model analysis predicted the maximum effect of PM2.5 exposure concentration on red blood cell count (RBC) and PLT was reached when the hysteresis accumulates for 1d (Lag0). The maximum effect of exposure concentration ofPM2.5 on MONO is reached when the lag accumulation is 3d (Lag2). When the hysteresis accumulates for 6d (Lag5), the exposure concentration of PM2.5 has the greatest effect on HGB. The maximum cumulative effect of PM2.5 on neutrophil count (NEUT) and lymphocyte (LMY) was strongest when the lag was 2d (Lag1). During periods of moderate to severe pollution, the concentration of water-soluble ions and heavy metal elements in PM2.5 increases significantly and has a significant correlation with some blood routine indicators.

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“…Consequently, there were 167, 117, 52 and 32 d for Type 1, Type 2, Type 3 and Type 4 over the study period, respectively. Since the WPSH's movement is generally affected by the weather phenomena of its surrounding climatic systems (such as typhoons and the Tibetan high) (Ge et al, 2019;Liu and You, 2020;Shu et al, 2016;Wang et al, 2019), it may have resulted in a shortterm southward retreat during the advancement of the WPSH (e.g., around 10 August 2018) and a short-term northward advancement during its process of retreat (e.g., 21 and 29 August 2016). For instance, tropical storm NEPARTAK generated at 00:00 UTC (08:00 BJT) 3 July 2016 over the western North Pacific and upgraded to a super typhoon at 12:00 UTC (20:00 BJT) 5 July 2016 (Fig.…”

Section: Objective Classification Of Large-scale Swps In Summermentioning

confidence: 99%

Large-scale synoptic drivers of co-occurring summertime ozone and PM<sub>2.5</sub> pollution in eastern China

et al. 2021

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Abstract. Surface ozone (O3) pollution during summer (June–August) over eastern China has become more severe in recent years, resulting in a co-occurrence of surface O3 and PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 µm in the air) pollution. However, the mechanisms regarding how the synoptic weather pattern (SWP) might influence this compound pollution remain unclear. In this study, we applied the T-mode principal component analysis (T-PCA) method to objectively classify the occurrence of four SWPs over eastern China, based on the geopotential heights at 500 hPa during summer (2015–2018). These four SWPs over eastern China were closely related to the western Pacific subtropical high (WPSH), exhibiting significant intra-seasonal and interannual variations. Based on ground-level air quality observations, remarkable spatial and temporal disparities of surface O3 and PM2.5 pollution were also found under the four SWPs. In particular, there were two SWPs that were sensitive to compound pollution (Type 1 and Type 2). Type 1 was characterized by a stable WPSH ridge with its axis at about 22∘ N and the rain belt located south of the Yangtze River Delta (YRD); Type 2 also exhibited WPSH dominance (ridge axis at ∼ 25∘ N) but with the rain belt (over the YRD) at a higher latitude compared to Type 1. In general, SWPs have played an important role as driving factors of surface O3–PM2.5 compound pollution in a regional context. Our findings demonstrate the important role played by SWPs in driving regional surface O3–PM2.5 compound pollution, in addition to the large quantities of emissions, and may also provide insights into the regional co-occurring high levels of both PM2.5 and O3 via the effects of certain meteorological factors.

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Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China

Cited by 71 publications

References 70 publications

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Effect of Different Pollution Parameters and Chemical Components of PM2.5 on Health of Residents of Xinxiang City, China

Large-scale synoptic drivers of co-occurring summertime ozone and PM<sub>2.5</sub> pollution in eastern China

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Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China

Cited by 71 publications

References 70 publications

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Multi-Year Concentrations, Health Risk, and Source Identification, of Air Toxics in the Venice Lagoon

Effect of Different Pollution Parameters and Chemical Components of PM2.5 on Health of Residents of Xinxiang City, China

Large-scale synoptic drivers of co-occurring summertime ozone and PM&lt;sub&gt;2.5&lt;/sub&gt; pollution in eastern China

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Large-scale synoptic drivers of co-occurring summertime ozone and PM<sub>2.5</sub> pollution in eastern China