Competing PM2.5 and NO2 holiday effects in the Beijing area vary locally due to differences in residential coal burning and traffic patterns

Abstract

The holiday effect is a useful tool to estimate the impact on air pollution due to changes in human activities. In this study, we assessed the variations in concentrations of fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ) during the holidays in the heating season from 2014 to 2018 based on daily surface air quality monitoring measurements in Beijing. A Generalized Additive Model (GAM) is used to analyze pollutant concentrations for 34 sites by comprehensively accounting for annual, monthly, and weekly cycles as well as the nonlinear impacts of meteorological factors. A Saturday effect was found in the downtown area, with about 4% decrease in PM 2.5 and 3% decrease in NO 2 relative to weekdays. On Sundays, the PM 2.5 concentrations increased by about 5% whereas there were no clear changes for NO 2 . In contrast to the small effect of the weekend, there was a strong holiday effect throughout the region with average increases of about 22% in PM 2.5 and average reductions of about 11% in NO 2 concentrations. There was a clear geographical pattern in the strength of the holiday effect. In rural areas the increase in PM 2.5 is related to the proportion of coal and biomass consumption for household heating. In the suburban areas between the Fifth Ring Road and Sixth Ring Road there were larger reductions in NO 2 than downtown which might be due to decreased traffic as many people return to their hometown for the holidays. This study provides insights into the pattern of changes in air pollution due to human activities. By quantifying the changes, it also provides insights for improvements in air quality due to control policies implemented in Beijing during the heating season.

Quantitative estimation of meteorological impacts and the COVID-19 lockdown reductions on NO2 and PM2.5 over the Beijing area using Generalized Additive Models (GAM)

“…The seasonal factors are therefore mostly related to humid weather conditions. As reported in a related study ( Hua et al, 2021b ), the opposite holiday effects of NO 2 and PM 2.5 were also observed. The interannual trends suggested that long-term control policies contributed to reductions of 26% for NO 2 and 46% for PM 2.5 from 2015 to 2020 during January to June.…”

“…The limit of model performance is determined by the uncertainty in the meteorology dataset, for example uncertainties in the simulation of the boundary layer height. The r 2 was somewhat lower and RMSE was somewhat higher for the hourly models than for the daily models ( Hua et al, 2021b ) mainly due to the high variability of hourly measurements and the greater difficulty in simulating the diurnal profiles. The lower model performance for the sites located in rural areas relative to the sites located in downtown and suburban can be seen in Table S1 .…”

“…For each site, the data availability was more than 90% over the entire period, which means there were more than 23,500 data points out of 26,112 h ( Table S1 ). The 34 air quality monitoring sites were classified into three groups based on urbanization levels ( Hua et al, 2021b ). The sites within the Fifth Ring Road were defined as downtown, and had the highest level of urbanization.…”

“…The types of functions for temporal variables characterized the change of emissions and the types of functions for meteorology characterized the relationship between meteorology and air quality. The selections of function type for interannual, seasonal, weekly, diurnal and meteorology were referenced from some previous studies ( Zhou et al, 2012 ; de Foy, 2018 ; de Foy and Schauer, 2019 ) and were also tested for the Beijing area in our recent study ( Hua et al, 2021b ). The interannual variable used a linear trend to characterize the decline in air pollutant concentrations.…”

The Impact of the COVID-19 Pandemic on Ambient Air Quality in China: A Quasi-Difference-in-Difference Approach

“…As the new year approaches, factories shut down and release their workers so they can travel home and spend time with their families during the Spring Festival (which was 24–30 January 2020) [ 25 ]. Besides, most industrial plants remain closed until the end of the holiday [ 26 ].…”