Characteristics of trace metal concentration and stable isotopic composition of hydrogen and oxygen in “urban-induced heavy rainfall” in downtown Tokyo, Japan; The implication of mineral/dust particles on the formation of summer heavy rainfall

Uchiyama, Ryunosuke; Okochi, Hiroshi; Ogata, Hiroko; Katsumi, Naoya; Nakano, Takanori

doi:10.1016/j.atmosres.2018.10.017

Cited by 16 publications

(6 citation statements)

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“…Sustained rainfall processes are often concomitant by changes in the accompanying meteorological factors such as temperature, humidity, wind speed and direction, which will affect the removal effect to varying degrees [33]. Therefore, in this paper, we conside the effect and intensity of the existing influence factors [34][35][36][37], and balance the availabil ity of their own observational data and the predictability of future trends to establish a se of accompanying influence factors F, including the (i) direct factor (FD) to describe rainfal characteristics, and the (ii) indirect factor (FI) to describe environmental characteristics The factors and impact effects are shown in Table 1. When the temperature near the ground is high, atmospheric convection is intensified, which tends to reduce PM2.5 concentrations, and conversely PM2.5 is not easily dispersed Humidity 𝐻 Changes in PM2.5 are closely related to the moisture content of the air, with "hygroscopic increase" occurring due to the adsorption of particulate matter concentrations The quantitative measurement steps are:…”

Section: Sustained Rainfall Removal Concomitant Factor Modelingmentioning

confidence: 99%

Section: Sustained Rainfall Removal Concomitant Factor Modelingmentioning

confidence: 99%

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

Xie

Xue

et al. 2021

Sustainability

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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.

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Section: Sustained Rainfall Removal Concomitant Factor Modelingmentioning

confidence: 99%

Section: Sustained Rainfall Removal Concomitant Factor Modelingmentioning

confidence: 99%

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

Xie

Xue

et al. 2021

Sustainability

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“…2). We calculated the uncertainty bounds of estimated rate constants using the 95 % error interval of prediction with a linear regression (Wackerly et al, 2014):…”

Section: Estimated Rate Constants (K 13 -K 17 )mentioning

confidence: 99%

Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis

et al. 2020

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Abstract. An estimated 0.5–1 billion people globally have inadequate intakes of selenium (Se), due to a lack of bioavailable Se in agricultural soils. Deposition from the atmosphere, especially through precipitation, is an important source of Se to soils. However, very little is known about the atmospheric cycling of Se. It has therefore been difficult to predict how far Se travels in the atmosphere and where it deposits. To answer these questions, we have built the first global atmospheric Se model by implementing Se chemistry in an aerosol–chemistry–climate model, SOCOL-AER (modeling tools for studies of SOlar Climate Ozone Links – aerosol). In the model, we include information from the literature about the emissions, speciation, and chemical transformation of atmospheric Se. Natural processes and anthropogenic activities emit volatile Se compounds, which oxidize quickly and partition to the particulate phase. Our model tracks the transport and deposition of Se in seven gas-phase species and 41 aerosol tracers. However, there are large uncertainties associated with many of the model's input parameters. In order to identify which model uncertainties are the most important for understanding the atmospheric Se cycle, we conducted a global sensitivity analysis with 34 input parameters related to Se chemistry, Se emissions, and the interaction of Se with aerosols. In the first bottom-up estimate of its kind, we have calculated a median global atmospheric lifetime of 4.4 d (days), ranging from 2.9 to 6.4 d (2nd–98th percentile range) given the uncertainties of the input parameters. The uncertainty in the Se lifetime is mainly driven by the uncertainty in the carbonyl selenide (OCSe) oxidation rate and the lack of tropospheric aerosol species other than sulfate aerosols in SOCOL-AER. In contrast to uncertainties in Se lifetime, the uncertainty in deposition flux maps are governed by Se emission factors, with all four Se sources (volcanic, marine biosphere, terrestrial biosphere, and anthropogenic emissions) contributing equally to the uncertainty in deposition over agricultural areas. We evaluated the simulated Se wet deposition fluxes from SOCOL-AER with a compiled database of rainwater Se measurements, since wet deposition contributes around 80 % of total Se deposition. Despite difficulties in comparing a global, coarse-resolution model with local measurements from a range of time periods, past Se wet deposition measurements are within the range of the model's 2nd–98th percentiles at 79 % of background sites. This agreement validates the application of the SOCOL-AER model to identifying regions which are at risk of low atmospheric Se inputs. In order to constrain the uncertainty in Se deposition fluxes over agricultural soils, we should prioritize field campaigns measuring Se emissions, rather than laboratory measurements of Se rate constants.

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“…Recent research on heavy metals found in dust fall has mainly focused on cities, parks, and roads ( Taylor, 2015 ; Parvizimehr et al, 2020 ; Zheng et al, 2010 ). Direct dust sample collection methods using air samplers can be used to determine heavy metal concentrations per unit area in a short period of time ( Mohsen, Ahmed & Zhou, 2018 ; Olawoyin et al, 2018 ), and indirect methods using dust tanks, collected rainwater, soil, plant leaves, or bark as biological indicators can be used to determine the long-term concentration of heavy metals in dust ( Uchiyama et al, 2019 ; Miri et al, 2016 ; Gholizadeh et al, 2019 ). Such long-term analytic methods are simple, easy to operate, inexpensive, and low risk.…”

Section: Introductionmentioning

confidence: 99%

Heavy metal pollution characteristics and health risk assessment of dust fall related to industrial activities in desert steppes

Wen-bao

et al. 2021

PeerJ

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China’s desert steppe is the transition zone between the grasslands in central China and the arid desert. Ecological security in this region has long been a subject of debate, both in the local and academic communities. Heavy metals and other pollutants are readily released during industrial production, combustion, and transportation, aggravating the vulnerability of the desert steppes. To understand the impact of industrial activiteis on the heavy metal content of dust fall in the desert steppe, a total of 37 dust fall samples were collected over 90 days. An inductively-coupled plasma mass spectrometer (NexION 350X) was used to measure the concentration of heavy metals Cu, Cd, Cr, Pb, Mn, Co, and Zn in the dust. Using comprehensive pollution index and multivariate statistical analysis methods, we explored the characteristics and sources of heavy metal pollution. We also quantitatively assessed the carcinogenic risks of heavy metals resulting from dust reduction with the help of health risk assessment models. The heavy metals’ comprehensive pollution index values in the study area’s dust fall were ranked as follows: Zn > Cd > Pb > Mn > Cu > Co > Cr. Among these, Zn, Cd, and Pb were significant pollution factors in the study area, and were affected by industrial production and transportation. The high pollution index was concentrated in the north of the research industrial park and on both sides of a highway. The seven heavy metals’ total non-carcinogenic risk index (HI) values were ranked as follows: Mn > Co > Pb > Zn > Cr > Cu > Cd (only the HI of Mn was greater than one). Excluding Mn, the non-carcinogenic and carcinogenic risk index values of the other six heavy metals were within acceptable ranges. Previous studies have also shown that industrial transportation and production have had a significant impact on the heavy metal content of dust fall in the desert steppe.

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Characteristics of trace metal concentration and stable isotopic composition of hydrogen and oxygen in “urban-induced heavy rainfall” in downtown Tokyo, Japan; The implication of mineral/dust particles on the formation of summer heavy rainfall

Cited by 16 publications

References 18 publications

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

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

Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis

Heavy metal pollution characteristics and health risk assessment of dust fall related to industrial activities in desert steppes

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