Assessment of meteorology vs control measures in China fine

12 A framework was developed to quantitatively assess the contribution of meteorology 13 variations to the trend of fine particular matter (PM2.5) concentrations and to separate the 14 impacts of meteorology from the control measures in the trend, based upon an 15 Environmental Meteorology Index (EMI). The model‐based index EMI realistically reflects 16 the role of meteorology in the trend of PM2.5 and is explicitly attributed into three major 17 factors: deposition, vertical accumulation and horizontal transports. Based on the 2013‐ 18 2019 PM2.5 observation data and re‐analysis meteorological data in China, the 19 contributions of meteorology and control measures in nine regions of China were 20 assessed separately by the EMI‐based framework.  Monitoring network observations 21 show that the PM2.5 concentrations have been declined about 50% on national average 22 and about 35% to 53% for various regions. It is found that the nation‐wide emission 23 control measures were the dominant factor in the declining trend of China PM2.5 24 concentrations, contributing to about 47% of the PM2.5 decrease from 2013 to 2019 on 25 2 | P a g e the national average and 32% to the 52% for various regions. The meteorology has a 1 variable and sometimes critical contribution to the year by year variations of PM2.5 2 concentrations, 5% on annual average and 10‐20% for the fall‐winter heavy pollution 3 seasons. 4

and about 35% to 53% for various regions. It is found that the nation-wide emission 23 control measures were the dominant factor in the declining trend of China PM 2.5 24 concentrations, contributing to about 47% of the PM2.5 decrease from 2013 to 2019 on 25 the national average and 32% to the 52% for various regions. The meteorology has a 1 variable and sometimes critical contribution to the year-by-year variations of PM 2.5 2 concentrations, 5% on annual average and 10-20% for the fall-winter heavy pollution 3 seasons. 4 1. Introduction 5 Recent observation data from the Ministry of Ecology and Environment of China (MEE) 6 has shown a steady improvement of air quality across the country, especially in particular 7 matter (PM) concentrations (Hou et al., 2019). According to 2013-2019 China Air Quality 8 Improvement Report issued by MEE, compared to 2013, the average concentrations of 9 particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) in 74 major 10 cities of China have decreased by more than 50% in 2019. From scientific and 11 management point of views, a quantitative apportionment of the reasons behind the 12 trend is critical to assess the reduction strategies implemented by the government and to 13 guide future air quality control policy. However, the assessment of the improvements of 14 air quality is a complicated process that involves the quantification of changes in the 15 emission sources, meteorological factors, and other characteristics of the PM2.5 pollution, 16 which are also interacting with each other. In order to separate the relative degree of 17 these factors, a comprehensive analysis, including observational data and model 18 simulation, is needed. 19 Researches have been done extensively on the impacts of weather systems on air 1 quality. Synoptic and local meteorological conditions have been recognized to influence 2 the PM concentrations at various scales ( 14 In the Beijing-Tianjin-Hebei (BTH) Region, a correlation analysis and principal 15 component regression method (Zhou et al., 2014) was used to identify the major 16 meteorological factors that influenced the API (Air Pollution Index) time series in China 17 from 2001-2010, indicating that air pressure, air temperature, precipitation and relative 18 humidity were closely related to air quality with a series of regression formulas. Yet, the 19 analysis was assumed a relatively unchanged emission whose impacts were not taken into 20 account. On a local scale, an attempt (Zhang et al., 2017) has been made to correlate the 21 air pollutant levels with a combination of meteorological factors with the development of 22 the Stable Weather Index (SWI) at CMA. The SWI is a composite index which includes the 1 advection, vertical diffusion and humidity and other meteorological factors that are 2 related to the formation of air pollutions in a specific region or city. A higher value of SWI 3 means a weaker diffusion of air pollutants. This index had some success in assessing the 4 meteorological impacts on air pollution, especially calibrated for a specific region, i.e. 5 Beijing. However, when applied to different areas where the emission patterns and 6 meteorological features are different, this index failed to give a universal or comparable 7 indication of meteorological assessment of pollution levels across the nation. 8 Using the Kolmogorov-Zurbenko (KZ) wave filter method, Bai et al (2015) separated 9 the API time series in three Chinese cities into short-term, seasonal and long-term 10 components, and then used the stepwise regression to set up API baseline and short-term 11 components separately and established linear regression models for meteorological 12 variables of corresponding scales. Consequently, with the long-term representing the 13 change of emissions removed from the time series, the meteorological contributions 14 alone were assumed and analyzed, pointing out that unfavorable conditions often lead to 15 an increase by 1-13 whereas the favorable conditions to a decrease by 2-6 in the long- 16 term API series, respectively. Though the contributions of emissions and meteorological 17 variations were separated by the research, it was only done by mathematical 18 transformations and far from the reality. The mechanisms behind the variation of the time 19 series were not investigated. 20 A chemical transport model (CTM) is an ideal tool to carry the task of assessment by 1 taking the meteorology, emissions and processes into considerations altogether. , nitrogen dioxide (NO 2 ) and PM and its constituents over Europe during 5 1958-2001. It is found that the average European interannual variation, due to 6 meteorological variability, ranges from 3% for O 3 , 5% for NO 2 , 9% for PM, 6-9% for dry 7 deposition, to about 20% for wet deposition of sulphur and nitrogen. A multi-model 8 assessment of air quality trends with constant anthropogenic emissions was also carried 9 out in Europe (Colette et al., 2011) and found that the magnitude of the emission-driven 10 trend exceeds the natural variability for primary compounds, concluding that that 11 emission management strategies have had a significant impact over the past 10 years,  18 2013. It was found that the change of source contribution of PM 2.5 in Beijing and northern 19 Hebei was dominated by the change of local emissions. However, for Tianjin, and central 20 and southern Hebei province, the change of meteorology condition was as important as 21 the change of emissions, illustrating the regional difference of impacts by meteorology 22 and emissions. However, the emission changes in the simulations were assumed and did 1 not reflect the real spatia-temporal variations. 2 There is no surprise that previous studies could not systematically catch the 3 meteorological impacts across the whole nation as the controlling meteorological factors 4 involving the characteristics of plenary boundary layers (PBL), wind speed and turbulence, 5 temperature and stability, radiation and clouds, underlying surface as well as pollutant 6 emissions, vary greatly from region to region. A single index or correlation cannot be 7 applied to the entire nation. Obviously, in order to systematically assess the impacts of 8 meteorology on air pollution, these factors have to be taken into consideration in a 9 framework and be assessed simultaneously. This paper presents a methodology to assess

15
The assessment is carried out through the combination of observational data and EMI 16 index from model analysis. Since the emission and air quality characteristics vary greatly 17 from region to region in China, the analysis is divided into 9 focused regions ( Figure 1). 18 Regional air quality dada (PM2.5) provides the basis for the trend analysis. Separating the 19 trend contribution from regional emission reduction and meteorological variation entails 1 a framework, which is discussed below.  (Table 1), with some regional differences. Regionally, by 2019, the PM2.5 reduction 3 rate from 2013 ranges from 35 to 53%. Detailed analysis will be given in the Results and 4 Discussions section. used to analyze the pollution meteorological conditions. When the daily average wind 8 speed is less than 2 m s -1 , a DSW day is defined. Since the haze formation is always related 9 to stable meteorological conditions and high aerosol mass loading, haze observation from 10 CMA is also used to analyze the haze trends and the impact of air quality on visibility. A 11 haze day is defined with daily averaged visibility less than 10 km and relative humidity less 12 than 85% (Wu et al., 2014), excluding days of low visibility due to precipitation, blowing 13 snow, blowing sand, floating dust, sandstorms and smoke. 14 The 2019 national annual averaged WS has increased by 4.5%, DSW dropped by 15 15.1%, and RH decreased by 3.9% compared with 2013, with regional differences (Table   16 1). Slightly changes occurred when compared with 2015 that WS has decreased by 0.7%, 17 DSW dropped by 11.3%, and RH decreased by 2.2%. Overall, it can be seen that the annual 18 haze days have a certain degree of correlations negatively with WS and positively with 19 DSW. Detailed analysis linking PM2.5 and meteorology will be given in the Results and 1 Discussions section.  Note: "+" increased； "-" decreased 5 In order to quantitatively assess the impacts of meteorological conditions to the 1 changes of air pollution levels, an index EMI (Environmental Meteorological Index) is 2 defined as follows. For a defined atmospheric column (h) at a time t, an EMI is defined 3 as an indication of atmospheric pollution level:

EMI -the Environmental Meteorological Index
(1) 6 where the EMI is the tendency that causes the changes of pollution level in a time 7 interval dt defined as: where the iEmid is the difference between emission and deposition, and iTran and 10 iAccu are the net (in minus out) advection transports and the vertical accumulation by 11 turbulent diffusion in the column, respectively. A positive sign of each factor indicates a 12 net flow of pollutants into the column, and vice versa. 13 Mathematically, these factors are expressed as: 18 where the tendency is normalized by a factor C0. For an application of EMI to the PM2.5, 1 C 0 is set to equal 35 g m -3 , the national standard for PM 2.5 in China (MEE, 2012), and 2 the EMI(t) is written as EMI(t)2.5. If the EMI2.5 is less than 1, the concentration level will 3 reach or be better than the national standard. 4 It can be seen here that these key parameters account for the major (4) 1 The relationship among the EMI, EMI(t) 2.5 and . is illustrated in Figure   2 3. It is clear that the EMI(t)2.5 is a function of time and can be used to reflect the 3 pollution level at any time t, while the . is the area under the EMI(t) 2.5 from 4 time t0 to t1, which gives the averaged pollution levels for the period. The derivatives of 5 EMI(t)2.5 are the EMI, which is a positive value when the pollution is being accumulated 6 and a negative value when the pollution is being dispersed. Therefore, for the period p with n discrete steps from t0 to t1, the .

10
represents the averaged meteorological influences on PM 2.5 , while the sum of the 11 positive EMI is the accumulation potentials and the sum of the negative EMI is the 12 dispersing potentials as illustrated in Figure 3. The relationship between them is derived 1 as follows: where n is the time steps in the period and the averaged EMI has been linked to the where PM (m0, e1) is a hypothetically non-measurable quantity, indicating the PM 9 concentration at p1 with emission e1 and meteorology m0, that does not exist in 10 reality. An assumption is to be made to compute this quantity using the EMIs. It is 11 assumed that: In order to quantitatively obtain each term defined in Equation 3, the CUACE 7 model was modified to extract the change rates for the processes involved. Driven by 8 the re-analysis meteorological data, the new system CUACE/EMI can be used to 9 calculate each term in EMI at each time step (t). 10 In summary, this section presents a systematic platform to separate and assess the 11 impacts of the meteorology and emissions on the ambient concentration changes. The

12
. and ∆EMIS form the basis for the assessment. In the Results and Discussions 13 section, the application of the platform is presented to assess the fine particular matter 14 (PM2.5) changes in China. 15   The worsening meteorological conditions represented by EMI 2.5 were also 4 supported by the observations for the two periods. The observed day with small wind 5 (DSW, wind speed less than 2 m s -1 ) reveals that, except for part of southern Hebei 6 province, the DSW increases 5-15 days for 2015 in most meteorological stations in BTH 7 region (Fig. 6a), which indicates a large decrease of local diffusion capability. The 8 comparison of wind rose map shows that the decrease of northwest wind and the 9 increase of southwest and northeast wind occurred in December 2015 (Fig. 6b). The    6 Regionally, the largest drop percentage of PM2.5 was seen in NEC and NWC regions (Fig.   7 8), reaching over 50% compared with 2013. In the BTH, BTH+, FWP and CEN regions, the 8 reduction was in the range of 45% to 50% while in YRD and PRD the reduction was 9 around 35%.  (Table 2). 2 Therefore, due to the diversity of meteorological conditions and emission distributions in 3 China, their impacts on ambient PM2.5 concentrations display unique reginal characteristics. 4 Overall, the emission controls are the dominant factor in contributing the decline trend in China 5 from 2013 to 2019. However, in certain regions or certain period of years, emissions were 6 found to be increased even with favorite meteorological conditions, which means the design of 7 national control strategies has to take both meteorology and emission impacts simultaneously 8 in order to achieve maximum results. 9