Spatiotemporal variability of NO<sub>2</sub> and PM<sub>2.5</sub> over Eastern China: observational and model analyses with a novel statistical method

Abstract

<p><strong>Abstract.</strong> Eastern China (27° N–41° N, 110° E–123° E) is heavily polluted by nitrogen dioxide (NO<sub>2</sub>), particulate matter with aerodynamic diameter below 2.5 μm (PM<sub>2.5</sub>) and other air pollutants. These pollutants vary in a variety of temporal and spatial scales, with many temporal scales non-periodic and non-stationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis-visualization package to evaluate the spatiotemporal variability of ground-level NO<sub>2</sub>, PM<sub>2.5</sub>, and their associations with meteorological processes over Eastern China in Fall–Winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China-wide synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or buildup of pollutants from one day to another. <br><br> We further evaluate simulations of GEOS-Chem and WRF/CMAQ in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO<sub>2</sub> by about 17 μg/m<sup>3</sup> and PM<sub>2.5</sub> by 35 μg/m<sup>3</sup> on average. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China-synchronous mode (especially for NO<sub>2</sub>). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dusts. CMAQ overestimates the diurnal cycle of pollutants due to too weak boundary layer mixing – especially in the nighttime, CMAQ overestimates NO<sub>2</sub> by about 30 μg/m<sup>3</sup> and PM<sub>2.5</sub> by 60 μg/m<sup>3</sup>. For the day-to-day variability, CMAQ reproduces the observed Eastern-China synchronous mode but not the north-south opposing mode of NO<sub>2</sub>. Both models capture the day-to-day variability of PM<sub>2.5</sub> better than that of NO<sub>2</sub>. These results shed light on model improvement. The EOF-EEMD package is freely accessible.</p>

Short-term effect of NO2 on outpatient visits for dermatologic diseases in Xinxiang, China: a time-series study

“…Another study involving 510,158 eczema visits reported that a 10 lg/m 3 increase in NO 2 (lag06) concentrations was associated with a 2.31% (95% CI: 1.17-3.45%) increase of outpatient visits (Li et al 2016). Liu et al (2016Liu et al ( , 2018a also observed that NO 2 concentrations were significantly associated with childhood atopic eczema and acne vulgaris in outpatients (Liu et al 2018b(Liu et al , 2016. A cross-sectional study conducted in France found that long-term exposure of NO 2 in children was significantly associated with lifetime eczema (Penard-Morand et al 2010).…”

“…With the rapid development of urbanization, industrialization, and motorization, Chinese cities have been heavily polluted (Feng et al 2018;Wang et al 2018;Liu et al 2018a). According to the report on the State of the Ecology and Environment in China (http:// english.mee.gov.cn/Resources/Reports/soe/), in 2015-2018, the average concentrations of PM 2.5 , PM 10 , O 3 , SO 2 , NO 2 and CO were 47 lg/m 3 , 81 lg/ m 3 , 151 lg/m 3 , 20 lg/m 3 , 35 lg/m 3 , and 1.8 mg/m 3 , respectively.…”

Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations

“…The Chinese official air quality monitoring network measures PM 2.5 and other pollutants since 2013, mostly in the urban areas. These data form the basis for many recent studies on the spatial and temporal characteristics of urban air pollution and their causes over China (Liu et al, 2018;Wang et al, 2014;Ge et al, 2018). However, these measurement data cannot be used to analyze long-term trends and variability of PM 2.5 and resulting health effects.…”

A Tsunami Warning System Based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

“…To confirm the requirement of the third criterion, we apply Empirical Orthogonal Function (EOF) analysis. EOF is commonly used to decompose the data into spatial and temporal modes in earth science (Liu et al, 2018; Lorentz, 1956). The EOF spatial modes provide information about the areas where the modal activity is the highest, which correspond to the main energy distribution of a system (Cohen et al, 2003).…”