Soil PM10 emission potential under specific mechanical stress and particles characteristics

Padoan, Elio; Maffia, Jacopo; Balsari, P.; Ajmone-Marsan, Franco; Dinuccio, Elio

doi:10.1016/j.scitotenv.2021.146468

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…Additionally, it revealed that PM 10 was not significantly associated with several heavy metals including Pb, Cu, Sn, Se, Ag, Tl, Mo, and Zn . However, PM 10 emerged as the key sensitive parameter for Be (1.00), Cd (1.00), Co (1.00), Ni (1.00), and V (1.00) (carcinogenic risk) and Be (0.51), Cr(III) (0.99), Ni (0.70), and V (0.93) (noncarcinogenic risk) due to the main exposure pathway being inhalation of Be, Ni, Cd, Co, and V soil contamination with harmful risk to the lung organic. , …”

Section: Resultsmentioning

confidence: 99%

“…36 However, PM 10 emerged as the key sensitive parameter for Be (1.00), Cd (1.00), Co (1.00), Ni (1.00), and V (1.00) (carcinogenic risk) and Be (0.51), Cr(III) (0.99), Ni (0.70), and V (0.93) (noncarcinogenic risk) due to the main exposure pathway being inhalation of Be, Ni, Cd, Co, and V soil contamination with harmful risk to the lung organic. 40,41 Detailed sensitive performance of BW a was calculated at incremental calculated ratios of 55, 60, 65, and 70 kg (vs. 61.8 kg) (Figure 3). The results showed that the maximum positive ratio for BW a was 1.00 for 1,2-dichloropropane and 1,1,2trichloroethane, while the maximum negative ratio was −0.43 for 1,2,3-trichloropropane.…”

Section: Calculation and Analysis Of The Key Parametersmentioning

confidence: 99%

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Multifaceted Insight into Sensitivity Analysis and Environmental Impact on Human Health of Soil Contamination Risk Assessment

Zhang,

Du,

Yuan

et al. 2023

Environ. Health

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Sensitivity analysis is a valuable method for evaluating the impact of model parameters on health risk characterization, thereby supporting the prediction of critical uncertainty factors. However, limitations arise in terms of cross-disciplinary discussions and in-depth analyses of previous research. To overcome these limitations, a systematic and multifaceted approach was introduced for analyzing the parameter sensitivities in soil contamination risk assessment. This approach specifically targeted the 12 main parameters associated with 65 soil contaminants for health risk assessment, employing detailed authoritative statistics for risk assessment. Screening analysis revealed that identified heavy metals and organics were influenced by key parameters, such as PM10, body weight of adults (BWa), daily air inhalation rate of adults (DAIRa), air exchange rate (ER), and typical soil parameters. PM10 showed a positive 100% correlation with inorganics and metals, but BWa and DAIRa exhibited different impacts on different chemicals, with an increase in potential risk observed with higher BWa and lower DAIRa. Furthermore, incorporating soil parameters in the analysis showed that compact soil could improve the protection against vapor organic compounds for human health. This refined study presents a comprehensive strategy for sensitivity analysis in health risk assessment of soil contamination, thereby offering substantial support for the protection and preservation of human health. A logical framework also was provided for addressing the limitations of sensitivity analysis and facilitating an understanding of the complex relationships between model parameters and the health risk of soil contamination.

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Section: Resultsmentioning

confidence: 99%

Section: Calculation and Analysis Of The Key Parametersmentioning

confidence: 99%

Multifaceted Insight into Sensitivity Analysis and Environmental Impact on Human Health of Soil Contamination Risk Assessment

Zhang,

Du,

Yuan

et al. 2023

Environ. Health

View full text Add to dashboard Cite

show abstract

“…The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/atmos14111644/s1, Figure S1: Performance evaluation of the ANN model using a Taylor diagram involving RMSE and correlation coefficient for southern Xinjiang; Table S1: Summary of published studies related to soil-derived dust emission related to PM 10 and PM 2.5 fractions in recent five years; Table S2: Summary of published ANN studies related to soil erosion; Table S3: Percentage of different sized particles during the sand-storm on 23 April 2014 in Taklimakan Desert [71][72][73][74][75][76][77][78][79][80][81][82][83][84][85].…”

Section: Supplementary Materialsmentioning

confidence: 99%

Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

Gao,

Liu,

Zhang

et al. 2023

Atmosphere

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Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM2.5 emissions from soil erosion, an approach based on an artificial neural network (ANN) model which provides a multilayered, fully connected framework that relates input parameters and outcomes was proposed in this study. Owing to the difficulty in obtaining the actual emissions of soil-derived PM10 and PM2.5 over a broad area, the PM10 and PM2.5 simulated results of the ANN model were compared with the published results simulated by the widely used wind erosion prediction system (WEPS) model. The PM10 and PM2.5 emission results, based on the WEPS, agreed well with the field data, with R2 values of 0.93 and 0.97, respectively, indicating the potential for using the WEPS results as a reference for training the ANN model. The calculated r, RMSE and MAE for the results simulated by the WEPS and ANN were 0.78, 3.37 and 2.31 for PM10 and 0.79, 1.40 and 0.91 for PM2.5, respectively, throughout Southern Xinjiang. The uncertainty of the soil-derived PM10 and PM2.5 emissions at a 95% CI was (−66–106%) and (−75–108%), respectively, in 2016. The results indicated that by using parameters that affect soil erodibility, including the soil pH, soil cation exchange capacity, soil organic content, soil calcium carbonate, wind speed, precipitation and elevation as input factors, the ANN model could simulate soil-derived particle emissions in Southern Xinjiang. The results showed that when the study domain was reduced from the entire Southern Xinjiang region to its five administrative divisions, the performance of the ANN improved, producing average correlation coefficients of 0.88 and 0.87, respectively, for PM10 and PM2.5. The performances of the ANN differed by study period, with the best result obtained during the sand period (March to May) followed by the nonheating (June to October) and heating periods (November to February). Wind speed, precipitation and soil calcium carbonate were the predominant input factors affecting particle emissions from wind erosion sources. The results of this study can be used as a reference for the wind erosion prevention and soil conservation plans in Southern Xinjiang.

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“…Atmospheric aerosols, based on their origin and on their particle size, are currently described by two main fractions: a coarse one, identified as PM 10 -PM 2.5 and a fine one, identified as PM 2.5 , which includes ultrafine (da < 0.1 µm) and accumulation mode particles (0.1-1 µm). Fine mode aerosol derives mainly from primary anthropogenic combustion sources and from secondary processes, whereas coarse mode PM derives from soil and road dust resuspension [32][33][34], and from natural sources, such as desert dust [35,36] and sea salt aerosol [37,38]. The two modes, given that their emission sources are completely different, are independent one from the other.…”

Section: Basic Scenario-local Pollution Sourcesmentioning

confidence: 99%

PM Dimensional Characterization in an Urban Mediterranean Area: Case Studies on the Separation between Fine and Coarse Atmospheric Aerosol

et al. 2022

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Fine particulate matter (PM) is object of particular attention due to its health effects. It is currently regulated by adopting PM2.5 as an indicator to control anthropogenic combustion emissions. Therefore, it is crucial to collect aerosol samples representative of such sources, without including PM from natural sources. Thus, a clean separation between coarse and fine mode aerosol should be set. With this purpose, aerosol size mass distribution was taken in the aerodynamic diameter range from 0.5 to 10 µm. In comparison with a base scenario, characterized by local pollution sources, three case studies were considered, involving desert dust advection, sea salt advection and forest fire aerosol from a remote area. In the base scenario, PM2.5 represented a suitable fine-mode indicator, whereas it was considerably affected by coarse PM in case of desert dust and sea salt aerosol advection. Such interference was considerably reduced by setting the fine/coarse separation at 1.0 µm. Such separation underrepresented fine PM from forest fire long-range transport, nonetheless in the case studies considered, PM1 represented the best indicator of fine aerosol since less affected by coarse natural sources. The data presented clearly support the results from other studies associating the health effects of PM2.5 to PM1, rather than to PM1–2.5. Overall, there is a need to reconsider PM2.5 as an indicator of fine atmospheric aerosol.

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Soil PM10 emission potential under specific mechanical stress and particles characteristics

Cited by 11 publications

References 24 publications

Multifaceted Insight into Sensitivity Analysis and Environmental Impact on Human Health of Soil Contamination Risk Assessment

Multifaceted Insight into Sensitivity Analysis and Environmental Impact on Human Health of Soil Contamination Risk Assessment

Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model

PM Dimensional Characterization in an Urban Mediterranean Area: Case Studies on the Separation between Fine and Coarse Atmospheric Aerosol

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