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High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM_2.5 Distribution in Beijing, China.

ABSTRACT: PM_2.5 is one of the primary components of air pollutants, and it has wide impacts on human health. Land use regression models have the typical disadvantage of low temporal resolution. In this study, various point of interests (POIs) variables are added to the usual predictive variables of the general land use regression (LUR) model to improve the temporal resolution. Hourly PM_2.5 concentration data from 35 monitoring stations in Beijing, China, were used. Twelve LUR models were developed for working days and non-working days of the heating season and non-heating season, respectively. The results showed that these models achieved good fitness in winter and summer, and the highest R² of the winter and summer models were 0.951 and 0.628, respectively. Meteorological factors, POIs, and roads factors were the most critical predictive variables in the models. This study also showed that POIs had time characteristics, and different types of POIs showed different explanations ranging from 5.5% to 41.2% of the models on working days or non-working days, respectively. Therefore, this study confirmed that POIs can greatly improve the temporal resolution of LUR models, which is significant for high precision exposure studies.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC8201188 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:High concentrations of particulate matter (PM2.5) and frequent air pollution episodes in Beijing have attracted widespread attention. This paper utilizes data from the new air pollution network in China to examine the current spatial and temporal variability of PM2.5 at 12 monitoring sites in Beijing over a recent 2-year period (April 2013) to March 2015). The long term (2-year) average concentration was 83 µg·m-3, well above Chinese and international standards. Across the region, annual average concentrations varied by 20 µg·m-3 (25% of the average level), with lower levels in suburban areas compared to periurban and urban areas, which had similar concentrations. The spatial variation in PM2.5 concentrations was associated with several land use and economic variables, including the fraction of vegetated land and building construction activity, which together explained 71% of the spatial variation. Daily air quality was characterized as "polluted" (above 75 µg·m-3) on 36 to 47% of days, depending on site. There were 77 pollution episodes during the study period (defined as two or more consecutive days with Beijing-wide 24-hour average concentrations over 75 µg·m-3), and 2 to 5 episodes occurred each month, including summer months. The longest episode lasted 9 days and daily concentrations exceeded 450 µg·m-3. Daily PM2.5 levels were autocorrelated (rlag1 = 0.516) and associated with many meteorological variables, including barometric pressure, relative humidity, hours of sunshine, surface and ambient temperature, precipitation and scavenging coefficient, and wind direction. Parsimonious models with meteorological and autoregressive terms explained over 60% of the variation in daily PM2.5 levels. The first autoregressive term and hours of sunshine were the most important variables in these models, however, the latter variable is PM2.5-dependent and thus not an explanatory variable. The present study can serve as a baseline to compare the improved air quality in Beijing expected in future years.

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High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM_2.5 Distribution in Beijing, China.

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Dataset Information

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM2.5 Distribution in Beijing, China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

High Temporal Resolution Land Use Regression Models with POI Characteristics of the PM_2.5 Distribution in Beijing, China.