Estimating historical <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msub><mml:mtext>PM</mml:mtext><mml:mn>2.5</mml:mn></mml:msub></mml:mrow></mml:math> exposures for three decades (1987–2016) in Japan using measurements of associated air pollutants and land use regression

Araki, Shin; Shima, Masayuki; Yamamoto, K.

doi:10.1016/j.envpol.2020.114476

Cited by 10 publications

(9 citation statements)

References 38 publications

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“…The seasonal spatial distribution of estimated PM 2.5 is presented in Figure S9. The PM 2.5 concentrations showed a decreased trend from 2011 to 2016 (Figure 7), which is consistent with the estimation in a previous study [46]. land use data to estimate monthly or annual mean PM2.5 concentrations.…”

Section: Pm 25 Estimatessupporting

confidence: 90%

“…China implemented an Air Pollution Prevention and Control Action Plan in 2013 with a series of stringent clean air actions during 2013-2017, which led to PM 2.5 rapidly decreasing across China [48]. In addition, the domestic emissions, especially those derived from road transport, decreased in Japan [46]. Hence, the decrease in both foreign and domestic emission sources may cause a long-term decreasing trend in Japan.…”

Section: Discussionmentioning

confidence: 99%

“…In 2020, Araki and colleagues in Japan used an artificial neural network and included meteorological variables (precipitation, wind speed, temperature, and relative humidity) and land use data (built-up area ratio, green area ratio, population density, distance to coastline, and longitude) as predictors to estimate monthly PM 2.5 concentrations during 1987-2016. Their model achieved a spatial and temporal cross-validation R 2 (RMSE) of 0.76 (1.9 µg/m 3 ) and 0.73 (2.0 µg/m 3 ), respectively [46]. The same group also applied the random forest model with ordinary kriging to estimate monthly PM 2.5 concentrations in Japan during 2010-2015 [50].…”

Section: Discussionmentioning

confidence: 99%

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A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

2021

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Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

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Section: Pm 25 Estimatessupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

2021

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“…Second, studies based on statistical models that used long-term ground visibility observations as input to back extrapolate PM 2.5 concentrations , were limited by the spatial coverage and uncertainty of the visibility data. Specifically, visibility data are limited by their relative inaccuracy in high values and inconsistency as they shifted from human observers to automated sensors. , Third, the time span of the training data set in some previous statistical exposure studies was less than 3 years, , which could hardly capture the interannual difference in air pollution levels. Lastly, many studies estimated PM 2.5 concentrations at coarse spatiotemporal resolutions (e.g., 0.25° × 0.25°) and annual mean, which could not produce spatiotemporal-resolved exposure metrics based on different exposure durations.…”

Section: Introductionmentioning

confidence: 99%

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

Liu,

Ma,

Gasparrini

et al. 2023

Environ. Sci. Technol.

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“…Historically, the most extensive monitoring systems were mainly developed in the United States and Western Europe, where regular monitoring of ambient air quality has been implemented since the mid-1970s [4]. Today, in Asia (especially Japan and China with a strong increase since the 2000s) there are extensive monitoring networks [5,6]. At the European level, the reference regulation for the monitoring and evaluation of air quality is set by Directive 2008/50/EC and subsequent amendments and additions [3].…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Designing Short-Term Stationary Air Quality Campaigns with Mobile Laboratories Using Different Possible Allocation Criteria

et al. 2021

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Air quality monitoring and control are key issues for environmental assessment and management in order to protect public health and the environment. Local and central authorities have developed strategies and tools to manage environmental protection, which, for air quality, consist of monitoring networks with fixed and portable instrumentation and mathematical models. This study develops a methodology for designing short-term air quality campaigns with mobile laboratories (laboratories fully housed within or transported by a vehicle and maintained in a fixed location for a period of time) as a decision support system for environmental management and protection authorities. In particular, the study provides a methodology to identify: (i) the most representative locations to place mobile laboratories and (ii) the best time period to carry out the measurements in the case of short-term air quality campaigns. The approach integrates atmospheric dispersion models and allocation algorithms specifically developed for optimizing the measuring campaigns. The methodology is organized in two phases, each of them divided into several steps. Fourteen allocation algorithms dedicated to three type of receptors (population, vegetation and physical cultural heritage) have been proposed. The methodology has been applied to four short-term air quality campaigns in the Emilia-Romagna region.

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Estimating historical PM2.5 exposures for three decades (1987–2016) in Japan using measurements of associated air pollutants and land use regression

Cited by 10 publications

References 38 publications

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019

A Methodology for Designing Short-Term Stationary Air Quality Campaigns with Mobile Laboratories Using Different Possible Allocation Criteria

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