scholarly journals Spatio-Temporal Variations of the PM2.5/PM10 Ratios and Its Application to Air Pollution Type Classification in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Hao Fan ◽  
Chuanfeng Zhao ◽  
Yikun Yang ◽  
Xingchuan Yang
Keyword(s):  
Air Pollution ◽  
Southern China ◽  
Spatial Scales ◽  
Mainland China ◽  
Temporal Variations ◽  
Average Concentration ◽  
Environmental Research ◽  
Observation Data ◽  
Important Indicator ◽  
Hu Line

Particulate Matter (PM) is an important indicator of the degree of air pollution. The PM type and the ratio of coarse and fine PM particles determine the ability to affect human health and atmospheric processes. Using the observation data across the country from 2015 to 2018, this study investigates the distribution and proportion of PM2.5 and PM10 at different temporal and spatial scales in mainland China; clarifies the PM2.5, PM10 and PM2.5/PM10 ratios interrelation; and classifies the dust, mixed, and anthropogenic type aerosol. It shows that the annual average concentration of PM2.5 and PM10 decreased by 10.55 and 8.78 μg m−3 in 4 years. PM2.5, PM10, and PM2.5/PM10 ratios show obvious while different seasonal variations. PM2.5 is high in winter and low in summer, while PM10 is high in winter and spring, and low in summer and autumn. Differently, the PM2.5/PM10 ratios are the highest in winter, and the lowest in spring. PM2.5/PM10 ratios show strong independence on PM2.5 and PM10, implying that it can provide extra information about the aerosol pollution such as aerosol type. A classification method about air pollution types is then further proposed based on probability distribution function (PDF) morphology of PM2.5/PM10 ratios. The results show that dust type mainly lies in the west of Hu-Line, mixed type pollution distributes near Hu-Line, and the anthropogenic type dominates over North China Plain and cities in southern China. The results provide insights into China’s future clean air policy making and environmental research.

scholarly journals A Quantitative Modeling and Prediction Method for Sustained Rainfall-PM2.5 Removal Modes on a Micro-Temporal Scale

2021 ◽  
Vol 13 (19) ◽  
pp. 11022
Author(s):  
Tingchen Wu ◽  
Xiao Xie ◽  
Bing Xue ◽  
Tao Liu
Keyword(s):  
Air Quality ◽  
Southern China ◽  
Prediction Method ◽  
Temporal Scale ◽  
Atmospheric Environment ◽  
Quantitative Modeling ◽  
Observation Data ◽  
Important Indicator ◽  
Modeling And Prediction ◽  
Prediction And Prevention

PM2.5 is unanimously considered to be an important indicator of air quality. Sustained rainfall is a kind of typical but complex rainfall process in southern China with an uncertain duration and intervals. During sustained rainfall, the variation of PM2.5 concentrations in hour-level time series is diverse and complex. However, existing analytical methods mainly examine overall removals at the annual/monthly time scale, missing a quantitative analysis mode that applies micro-scale time data to describe the removal phenomenon. In order to further achieve air quality prediction and prevention in the short term, it is necessary to analyze its micro-temporal removal effect for atmospheric environment quality forecasting. This paper proposed a quantitative modeling and prediction method for sustained rainfall-PM2.5 removal modes on a micro-temporal scale. Firstly, a set of quantitative modes for sustained rainfall-PM2.5 removal mode in a micro-temporal scale were constructed. Then, a mode-constrained prediction of the sustained rainfall-PM2.5 removal effect using the factorization machines (FM) was proposed to predict the future sustained rainfall removal effect. Moreover, the historical observation data of Nanjing city at an hourly scale from 2016 to January 2020 were used for mode modeling. Meanwhile, the whole 2020 year observation data were used for the sustained rainfall-PM2.5 removal phenomenon prediction. The experiment shows the reasonableness and effectiveness of the proposed method.

scholarly journals A study of air pollution with formaldehyde along the highways in Kyiv city.

2018 ◽  
Vol 2 (2) ◽  
pp. 1-7 ◽  
Author(s):  
Olena Voloshkina ◽  
Rostyslav Sipakov ◽  
Olena Zhykova ◽  
Julia Bereznitska
Keyword(s):  
Air Pollution ◽  
Average Concentration ◽  
Observation Data ◽  
Hydrocarbon Emission

The problem of air pollution in the cities of Ukraine was described. The methodology of calculation of hydrocarbon emission on automobile overpasses and crossroads of Kyiv was suggested. By the number of bands, the number of cars was determined. The calculation has shown that at the same time near 300 cars can be on the overpass during peak hours. The average concentration of formaldehyde according to the calculations and observation data in 2016 was presented.

scholarly journals Spatial and Temporal Characteristics of PM2.5 Sources and Pollution Events in a Low Industrialized City

2021 ◽  
Vol 20 (3) ◽  
Author(s):  
R. Xu ◽  
Q. Tian ◽  
H. Wan ◽  
J. Wen Wen ◽  
Q. Zhang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Spatial Interpolation ◽  
Southern China ◽  
Interpolation Method ◽  
Average Concentration ◽  
Air Pollution Control ◽  
Hysplit Model ◽  
Mountain System ◽  
External Sources ◽  
Spatial Interpolation Method

In recent years, cities in southern China have experienced severe air pollution, despite having few sources of pollutants. To study the pollution characteristics of PM2.5 in these “low industrialized” cities, a numerical method based on the HYSPLIT4 Model and Kriging Spatial Interpolation Technology was established. Simulation results showed that the PM2.5 pollution in Guilin was affected by both internal and external sources. The backward air mass trajectory from July 2017 to June 2018 was simulated using the HYSPLIT model. The cluster analysis results indicated that the direction of trajectory ? accounted for 63.09% of the air pollution in the city. The average concentration of PM2.5 pollution was 45.94 ?g.m-3. The pollutant originated from the “Xiang-Gui Corridor.” The location of the sources was collocated with high industry regions. The spatial characteristics of the four pollution processes in the winter of 2017 were analyzed using a spatial interpolation method. The results showed that the transport of air masses in the direction of trajectory ? was obstructed by a mountain system in the northeast. Therefore, two air pollution accumulation centers and a topographic weakening zone dominated by internal and external sources were formed. It can be inferred that the air pollution in Guilin is affected by both internal and external factors. These results provide important theoretical and technical support for regional air pollution control and environmental protection.

scholarly journals Impact of COVID-19 on PM2.5 Pollution in Fastest-growing Megacity Dhaka, Bangladesh

2021 ◽  
pp. 1-11
Author(s):  
Showmitra Kumar Sarkar ◽  
Md. Mehedi Hasan Khan
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Historical Data ◽  
Fine Particulate Matter ◽  
Average Concentration ◽  
Behavioral Changes ◽  
Observation Data ◽  
Fine Particulate ◽  
The Impact ◽  
Absolute Decrease

Abstract Objective: The purpose of the research was to investigate and identify the impact of COVID-19 lockdown on fine particulate matter (PM2.5) pollution in Dhaka, Bangladesh by using ground-based observation data. Methods: The research assessed air quality during the COVID-19 pandemic for PM2.5 from 1 January 2017 to 1 August 2020. The research considered pollution in pre-COVID-19 (1 January-23 March), during COVID-19 (24 March-30 May), and post-COVID-19 (31 May-1 August) lockdown periods with current (2020) and historical (2017-2019) data. Results: PM2.5 pollution followed a similar yearly trend in year 2017-2020. The average concentration for PM2.5 was found 87.47 μg/m3 in the study period. Significant PM2.5 declines were observed in the current COVID-19 lockdown period compared to historical data: 11.31% reduction with an absolute decrease of 7.15 μg/m3. Conclusion: The findings of the research provide an overview of how the COVID-19 pandemic affects air pollution. The results will provide initial evidence regarding human behavioral changes and emission controls. This research will also suggest avenues for further study to link the findings with health outcomes.

scholarly journals The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

2021 ◽  
Author(s):  
Qingqing He ◽  
Mengya Wang ◽  
Steve Hung Lam Yim
Keyword(s):  
Spatial Scales ◽  
Pearson Correlation ◽  
Eastern China ◽  
Atmospheric Correction ◽  
Mainland China ◽  
Temporal Variations ◽  
Central China ◽  
Impact Factors ◽  
Considerable Uncertainty ◽  
The Relationship

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring surface PM2.5 concentration due to its extensive spatial and temporal coverage. Satellite-derived PM2.5 estimation strongly relies on an accurate representation of the relationship between ground PM2.5 and satellite aerosol optical depth (AOD). Due to the limitation of satellite AOD data, most studies examined the relationship at a coarse-resolution (i.e., ≥ 10 km) scale; more effort is still needed to better understand the relationship between in-situ PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Considerable uncertainty therefore still exists in satellite-derived PM2.5 estimation due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD and its plausible impact factors including meteorological parameters and topography in mainland China during 2019, at various spatial and temporal scales. Coefficient of variation, Pearson correlation coefficient and slope of linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China and linear slope in northern inland regions was on average larger than those in other areas. Temporally, the PM2.5-AOD correlation peaked in the noon and afternoon and reached the maximum in winter. Simultaneously considering relative humidity (RH) and planetary boundary layer height (PBLH) in the relationship can improve the correlation but the effect of RH and PBLH on the correlation varied spatially and temporally, both in strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as Sichuan Basin, Shanxi-Shaanxi Basins and Junggar Basin. MAIAC 1-km AOD can better represent the ground-level fine particulate matter in most domains with exceptions such as in very high terrain i.e. Tibetan Plateau and north-central China i.e. Qinghai and Gansu. Findings in this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status in mainland China.

scholarly journals A global association between Covid-19 cases and airborne particulate matter at regional level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Angelo Solimini ◽  
F. Filipponi ◽  
D. Alunni Fegatelli ◽  
B. Caputo ◽  
C. M. De Marco ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Negative Binomial ◽  
Numerical Models ◽  
Regional Level ◽  
Pollution Level ◽  
Observation Data ◽  
Cumulative Number ◽  
Mixed Effect

AbstractEvidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM10, PM2.5), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m3 increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM2.5 and PM10 respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.

scholarly journals Evaluations of Surface PM10 Concentration and Chemical Compositions in MERRA-2 Aerosol Reanalysis over Central and Eastern China

2021 ◽  
Vol 13 (7) ◽  
pp. 1317
Author(s):  
Xiaodan Ma ◽  
Peng Yan ◽  
Tianliang Zhao ◽  
Xiaofang Jia ◽  
Jian Jiao ◽  
...  
Keyword(s):  
Eastern China ◽  
Mainland China ◽  
Chemical Components ◽  
Chemical Compositions ◽  
Observation Data ◽  
Causative Factors ◽  
Routine Sampling ◽  
Aerosol Compositions ◽  
Modern Era

The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013.

scholarly journals Outdoor air pollution and diminished ovarian reserve among infertile Korean women

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Hannah Kim ◽  
Seung-Ah Choe ◽  
Ok-Jin Kim ◽  
Sun-Young Kim ◽  
Seulgi Kim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Ovarian Reserve ◽  
Air Pollutants ◽  
Total Population ◽  
Secondary Analysis ◽  
Reproductive Potential ◽  
Ambient Air ◽  
Average Concentration ◽  
Ambient Air Pollution ◽  
Korean Women

AbstractBackgroundMounting evidence implicates an association between ambient air pollution and impaired reproductive potential of human. Our study aimed to assess the association between air pollution and ovarian reserve in young, infertile women.MethodsOur study included 2276 Korean women who attended a single fertility center in 2016–2018. Women’s exposure to air pollution was assessed using concentrations of particulate matter (PM10and PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) that had been collected at 269 air quality monitoring sites. Exposure estimates were computed for 1, 3, 6, and 12 months prior to the ovarian reserve tests. Anti-Müllerian hormone (AMH) ratio (defined as an observed-to-expected AMH based on age) and low AMH (defined as < 0.5 ng/mL) were employed as indicators of ovarian reserve. We included a clustering effect of 177 districts in generalized estimating equations approach. A secondary analysis was conducted restricting the analyses to Seoul residents to examine the association in highly urbanized setting.ResultsThe mean age was 36.6 ± 4.2 years and AMH level was 3.3 ± 3.1 ng/mL in the study population. Average AMH ratio was 0.8 ± 0.7 and low AMH was observed in 10.3% of women (n=235). The average concentration of six air pollutants was not different between the normal ovarian reserve and low AMH groups for all averaging periods. In multivariable models, an interquartile range (IQR)-increase in 1 month-average PM10was associated with decrease in AMH ratio among total population (β= −0.06, 95% confidence interval: −0.11, 0.00). When we restrict our analysis to those living in Seoul, IQR-increases in 1 and 12 month-average PM2.5were associated with 3% (95% CI: −0.07, 0.00) and 10% (95% CI: −0.18, −0.01) decrease in AMH ratio. The ORs per IQR increase in the six air pollutants were close to null in total population and Seoul residents.ConclusionsIn a cohort of infertile Korean women, there was a suggestive evidence of the negative association between ambient PM concentration and ovarian reserve, highlighting the potential adverse impact of air pollution on women’s fertility.

scholarly journals Temporal and spatial variation on heavy metal concentrations in the oyster Ostrea equestris on the northern coast of Rio de Janeiro State, Brazil

2005 ◽  
Vol 65 (1) ◽  
pp. 67-76 ◽  
Author(s):  
A. G Ferreira ◽  
A. L. S. Machado ◽  
I. R. Zalmon
Keyword(s):  
Heavy Metal ◽  
Spatial Variation ◽  
Rio De Janeiro ◽  
Dry Weight ◽  
Temporal Variations ◽  
Average Concentration ◽  
Northern Coast ◽  
High Concentrations ◽  
Janeiro State ◽  
Ostrea Equestris

Heavy metal (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) concentrations were determined by ICP-AES in Ostrea equestris from three beaches (Barra do Furado, Buena, and Ponta do Retiro) on the northern coast of Rio de Janeiro State. The average concentration was 0.8 ± 0.18, 0.4 ± 0.21, 58 ± 25.6, 249 ± 52.3, 11 ± 1.31, 0.55 ± 0.16, 0.13 ± 0.11, and 1131 ± 321 µg.g-1 dry weight for Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn respectively. Significant spatial variation (p < 0.05) between the samples areas occurred for Cr, Pb, and Zn with higher values in Barra do Furado; and for Cu in Ponta do Retiro. Significant temporal variations (p < 0.05) were observed for all metals except Cu. Temporal variability may be related to changes in the inputs of metals associated with suspended particles. Concentrations were similar to those found in areas under low pollution impact, except for Zn, the high concentrations of which probably reflect the physiological characteristics of these organisms.

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