Prediction of health effects of cross-border atmospheric pollutants using an aerosol forecast model

Onishi, Kazunari; Sekiyama, T. T.; Nojima, Masanori; Kurosaki, Yasunori; Fujitani, Y; Otani, Shinji; Mäki, T.; Shinoda, Masato; Kurozawa, Youichi; Yamagata, Zentaro

doi:10.1016/j.envint.2018.04.035

Cited by 9 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, the comparison can validate not only local metal emissions but also emissions from different sources transported to the site. As presented later in section 4.4, the long‐term variation in the surface concentration at Yonago could be dominantly affected by transboundary air pollution (e.g., Onishi et al, 2018). The site is also under the influence of massive transport of Asian dust in spring (section 4.4; e.g., Kajino, Deushi, et al (2019)).…”

Section: Resultsmentioning

confidence: 99%

Modeling Transition Metals in East Asia and Japan and Its Emission Sources

et al. 2020

Self Cite

View full text Add to dashboard Cite

Emission inventories of anthropogenic transition metals, which contribute to aerosol oxidative potential (OP), in Asia (Δx ¼ 0.25°, monthly, 2000-2008) and Japan (Δx ¼ 2 km, hourly, mainly 2012) were developed, based on bottom-up inventories of particulate matters and metal profiles in a speciation database for particulate matters. The new inventories are named Transition Metal Inventory (TMI)-Asia v1.0 and TMI-Japan v1.0, respectively. It includes 10 transition metals in PM 2.5 and PM 10 , which contributed to OP based on reagent experiments, namely, Cu, Mn, Co, V, Ni, Pb, Fe, Zn, Cd, and Cr. The contributions of sectors in the transition metals emission in Japan were also investigated. Road brakes and iron-steel industry are primary sources, followed by other metal industry, navigation, incineration, power plants, and railway. In order to validate the emission inventory, eight elements such as Cu, Mn, V, Ni, Pb, Fe, Zn, and Cr in anthropogenic dust and those in mineral dust were simulated over East Asia and Japan with Δx ¼ 30 km and Δx ¼ 5 km domains, respectively, and compared against the nationwide seasonal observations of PM 2.5 elements in Japan and the long-term continuous observations of total suspended particles (TSPs) at Yonago, Japan in 2013. Most of the simulated elements generally agreed with the observations, while Cu and Pb were significantly overestimated. This is the first comprehensive study on the development and evaluation of emission inventory of OP active elements, but further improvement is needed. Plain Language Summary Aerosol oxidative potential (OP) has been focused on as a better health hazard of aerosols than PM 2.5. OP is quantified by in vitro assays to mimic the in vivo generation of superoxide radicals to cause oxidative stress on human cells. OP has been reported to be more strongly associated with cardiorespiratory outcomes than PM 2.5. Transition metals ions, organics, and elementary carbon, together with their interactions, have been reported as important compounds, but the relative magnitudes of contributions to total OP have not yet been fully understood. In this study, as a first step, we developed emission inventories and a numerical model to predict OP active transition metals in Asia and Japan and compared them with observations. We also quantitatively derived the major emission sources for the important metals such as Cu, Mn, Fe, V, and Ni in Japan. The current study is the first step toward better predicting the health hazard of aerosols. As the next step in the future, water solubility of metals and OP active organics, together with their interactions, should be considered.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling Transition Metals in East Asia and Japan and Its Emission Sources

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The result could be driven by cross-border pollution as Jakarta is surrounded by outskirt areas such as Bogor, Depok, Tangerang, Bekasi, Karawang (8) as urban agglomeration cluster that still had a high intensity of industrial, commercial, office work, transportation and domestic activity during the pandemics 2020-2021 (26). The result is supported by similar findings from other cities, for instance, aerosols air pollutants generated from mainland China reached Japan after few hours, and it was supported by the favourable wind direction and meteorological condition (27).…”

Section: Cross Correlation Analysismentioning

confidence: 99%

Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Husnina

Wangdi²,

Puspita³

et al. 2023

JKL

View full text Add to dashboard Cite

Introduction: Jakarta has recorded heightened air pollution for years, and particulate matter (PM10) is one of the pollutants that could bring health burden in population. This study described the distribution of PM10 as well as analysed the correlation with meteorological parameters during 2020–2021 in Jakarta Province. Methods: Air quality standard index daily data from January 1st 2020 to March 31st 2021 was retrieved from the official data portal (https://data.jakarta.go.id/). The Spearman Rank correlation was employed to understand the correlation between PM10 Index with meteorological factors. Autoregressive Integrative Moving Average (ARIMA) model was constructed and Akaike Information Criterion (AIC) selected the model. Cross-correlation analysis explored the association between PM10 with meteorological parameters at multiple time lags. Results and Discussion: PM10 Index started to increase in April 2020 and reached its peak in August 2020. PM10 was positively correlated with temperature (p-value <0.05, R2: 0.134), but it was negatively correlated with humidity and wind speed (p-value <0.05, R2: -0.392 and -0.129). The high cross-correlation coefficients were found between PM10 and temperature at lag 0, humidity at lag 1 and wind speed at lag 1 (rho: 0.42, -0.38 and -0.24). The time series model ARIMA with parameter (p,d,q) (1,1,1) describes the fluctuation of PM10 index data with AIC 3552.75. Conclusion: PM10 concentration in Jakarta is significantly correlated with meteorological factors. The implementation of social restriction in Jakarta need to be supported by pollution control in the neighbouring areas in order to be able to reduce PM10 pollution level.

show abstract

“…Onishi et al [28] examined the relationship between subjective health symptoms and aerosol data obtained from the Model of aerosol species in the global atmosphere (MASINGAR) provided by the Japan Meteorological Agency. The purpose of this study is to apply these data to predict the health effects of atmospheric pollutant transport.…”

Section: Introductionmentioning

confidence: 99%