Visibility-based PM2.5 concentrations in China - ACS Publications

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Visibility-based PM2.5 concentrations in China: 1957-1964 and 1973-2014 Miaomiao Liu, Jun Bi, and Zongwei Ma Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b03468 • Publication Date (Web): 24 Oct 2017 Downloaded from http://pubs.acs.org on October 24, 2017

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Environmental Science & Technology

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Visibility-based PM2.5 concentrations in China: 1957-1964 and

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1973-2014

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Miaomiao Liu a, Jun Bi a,b, Zongwei Ma a, c,*

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a

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Nanjing University, Nanjing, Jiangsu, China

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b

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Technology (CICAEET), Jiangsu, China

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c

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China

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment,

Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment

School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu,

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*Correspondence to:

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Dr. Zongwei Ma, School of the Environment, Nanjing University, 163 Xianlin Avenue,

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Nanjing 210023, P. R. China. Tel.: +86 25 89681526. E–mail address: [email protected].

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1

ACS Paragon Plus Environment


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ABSTRACT

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China established ground PM2.5 monitoring network in late 2012 and hence the long-term and

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large-scale PM2.5 data were lacking before 2013. In this work, we developed a national-scale

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spatiotemporal linear mixed effects model to estimate the long-term PM2.5 concentrations in China

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from 1957 to 1964 and from 1973 to 2014 using ground visibility monitoring data as the primary

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predictor. The overall model-fitting and cross-validation R2 is 0.72 and 0.71, suggesting that the model

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is not over-fitted. Validation beyond the model year (2014) indicated that the model could accurately

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estimate historical PM2.5 concentrations at the monthly (R2 = 0.71) level. The historical PM2.5

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estimates suggest that air pollution is not a new environmental issue that occurs in the recent decades

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but a problem existing in a longer time before 1980. The PM2.5 concentrations have reached 60-80

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µg/m3 in the north part of North China Plain during 1950s-1960s and increased to generally higher

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than 90 µg/m3 during 1970s. The results also show that the entire China experienced an overall

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increasing trend (0.19 µg/m3/yr, P 90% to avoid the

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influence of potential fog or rain events based on the RH and precipitation data 2. The sources and purposes

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of all datasets are listed in Supporting Information (SI, Table S1). The hourly data were then aggregated

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into daily average for further analysis. Since the NCEI meteorological data from 1965 to 1972 are totally

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missing, we did not modeled and analyzed the historical PM2.5 for this period. It should be noted that there

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was still some missing data for the period from 1957 to 1964 and from 1973 to 2014. There could be more

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missing data for some sites than others.

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Figure 1. Spatial distribution of ground PM2.5 (A) and meteorological (B) monitoring sites involved in this study.

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Data integration

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To obtain more matched data points and develop a robust statistical model, we first created a radius

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buffer zone for each single meteorological site to match PM2.5 and meteorological data. For each day,

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the daily PM2.5 concentrations within the buffer zone of a single meteorological site were averaged and 7



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assigned to the corresponding single meteorological site. Then the buffer-averaged PM2.5 was matched

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to the meteorological variables in that meteorological site. We conducted a sensitivity analysis to find

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the optimal buffer radius for data matching. We created a series of radius buffer zones ranged from

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5km to 70km and compared the cross-validation (CV) R2 results for models developed using datasets

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from different radius buffer matching strategy (SI, Text S1 and Figure S1). Results show that 20 km is

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the optimal buffer radius which can achieve the best model performance.

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China established ground PM2.5 monitoring network in late 2012, thus we have relatively complete

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PM2.5-visibility matched dataset covering entire China in 2013 and 2014 to fit the model. When using

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the model developed from the two-year data to estimate the historical PM2.5 concentrations, we need to

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evaluate the historical estimates using ground measurements before 2013. Although we have ground

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PM2.5 measurements from 2004 to 2012, most of them located in Hong Kong and Taiwan. Therefore,

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using ground monitoring data from 2004 to 2012 cannot represent the performance of historical

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estimates for the whole China. Thus, we used matched data of 2014 to fit and cross validate the model

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in the modeling year. We matched and compared the visibility-based PM2.5 historical estimates with

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the ground PM2.5 measurements using the same buffer matching strategy as mentioned above from

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2004 to 2013 to evaluate the accuracy of historical PM2.5 estimates.

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Model development and validation

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We developed a spatial-temporal linear mixed effects (LME) model to fit the month- and

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province-specific and random intercepts and random slopes for the log-transformations of visibility

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using the matched daily PM2.5 and daily visibility data. In other words, we established the relationship

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between daily average PM2.5 concentration and daily mean visibility range for each month in a 8


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province. The model structure is shown as follow:

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PM2.5,st = (µ +µ’ij) + (β1 +β1’ij) lnVISst + εst (µ’ij,β1’ij)~N[(0,0), Ψ]

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where PM2.5,st is the daily ground PM2.5 concentration at site s on day t; lnVISst is log-transformation of

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visibility range at site s on day t; µ is the fixed intercept; µ’ij is the month- and province-specific

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random intercept, i.e., the regression intercept in province i in month j; β1-β2 are fixed slopes for

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independent valuables; β1’ij is the random slope for log-transformation of visibility range in province i

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in month j; εst is the error term at site s on day t. Ψ is the unstructured variance-covariance matrix for

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the random effects. The selection of lnVIS as the only independent variable is based on the comparison

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of models using different combinations of independent variables (SI, Text S2 and Table S3).

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Statistical indicators of coefficient of determination (R2) and root mean squared prediction error

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(RMSE) were calculated to assess the performance of model fitting. We used 10-fold CV to test

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potential model over-fitting, that is, the model might have better predictive performance in the model

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dataset than in the rest of the data from the study area. First, all samples in the model dataset were

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randomly and equally divided into ten subsets. For the first round, one subset was selected as the

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testing dataset while the remaining nine subsets were used as the model dataset. We fitted the model

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using the nine subsets and applied the model on the visibility data in the testing dataset to predict

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PM2.5 concentrations. For the next round, another subset was used as the testing dataset and the

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remaining nine subsets were used to fit the model. This process was repeated ten rounds until each

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subset was used exactly once for validation. The agreement between CV-predicted PM2.5 and ground

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observations was evaluated using R2 and RMSE. Statistics of R2 and RMSE between model fitting and

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CV were compared to assess the potential model over-fitting.

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[1]


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Estimation, evaluation, and time series analysis of historical PM2.5

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The historical daily PM2.5 concentrations (1957-1964, 1973-2013) were estimated by applying the

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model developed in 2014 on the historical visibility data. Note that data from 1965 to 1972 are missing

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and therefore we cannot estimate the PM2.5 concentrations during this period. Although the model has

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been cross validated in the modeling year of 2014, it does not mean that historical (before 2014) PM2.5

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estimates would have similar accuracy as the model CV results. Therefore, we evaluated historical

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PM2.5 estimations at the daily and monthly scales using ground-measured PM2.5 from 2004-2013.

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We deseasonalized the historical PM2.5 data by calculating the monthly PM2.5 anomaly time series for

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each site to remove the influence of the seasonal effect. The monthly anomaly was calculated by

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subtracting the long-term average PM2.5 concentration of the corresponding month from the monthly

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PM2.5 estimate. We then applied least squares regression to the monthly PM2.5 anomaly time series for

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each site to analyze the PM2.5 trends 21, which has been applied to analyzed the satellite AOD or

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satellite-based PM2.5 trends 5, 22, 23. We also calculated the national and regional (including Jing-Jin-Ji

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region, Yangtze River Delta, and Pearl River Delta) trends. We first obtained the national or regional

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averaged monthly PM2.5 anomalies and then calculated the national and regional trends.

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RESULTS AND DISCUSSION

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Descriptive statistics of model fitting dataset

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Descriptive statistics of the model fitting dataset are shown in Table S3 (SI). Using the 20-km radius

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buffer matching strategy, we obtained 28,098 matched data records to fit the model. The overall mean

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PM2.5 concentration is 56.74 µg/m3 and the mean value of visibility is 13.18 km. The high standard

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deviations of PM2.5 (47.27µg/m3) and visibility (7.88 km) indicate the strong spatial and temporal

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variations of PM2.5 pollutions in China. The values exhibit strong seasonality. The highest mean PM2.5

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concentration is in winter (81.18 µg/m3), with the lowest mean visibility (11.37 km). Summer

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possesses the lowest mean PM2.5 (40.37 µg/m3) and the highest mean visibility (14.55 km). Spring and

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autumn presents similar PM2.5 and visibility levels. The seasonal variations are consistent with

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previous studies 8, 24, 25. Central heating with coal burning combined with frequent stagnant weather

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and temperature inversion is the main reason for high PM2.5 pollution levels in winter China24, 26.

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Model fitting and cross validation

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Figure 2 summarized the model fitting and CV results. For the model fitting, the R2 value is 0.72 and

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the RMSE is 24.86 µg/m3. The CV R2 value is 0.71 and the CV RMSE is 25.62 µg/m3. The differences

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between model fitting and CV R2 (or RMSE) values are often used to assess the degree of model

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overfitting. Compared to the model fitting, the CV R2 only decreases by 0.01 and CV RMSE only

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increases by 0.76 µg/m3, indicating that our model is not substantially over-fitted. The model CV

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results show that the model has satisfactory performance when predicting PM2.5 concentrations in

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those meteorological sites which are not included in the model dataset in the model year of 2014.

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Figure 2. Density scatterplots of model fitting (A) and cross-validation (B) (N = 28,098).

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Table S4 and S5 summarizes the fixed effects and variance-covariance matrix of random effects for

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the LME model, respectively. Results show that the log-transformed visibility is significant predictor

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for PM2.5 concentrations. Higher PM2.5 concentrations would cause lower visibility in the days with

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relative humidity less than 90% and without rains. This finding is consistent with our common sense

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and previous studies 13, 27, suggesting that our model can be physically explained.

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We also fitted models only with month-specific or province-specific random effects for comparison.

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For model only with month-specific random effects, the model fitting and CV R2 values are 0.45 and

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0.44, respectively. For the province-specific model, the model fitting and CV R2 values are 0.57 and

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0.56, respectively. Results showed that combing month- and province-specific random effects can

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greatly improve the model performance. Besides, we also fitted the geographically weighted

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regression (GWR) model for comparison. The overall model fitting R2 of the GWR model is 0.83,

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which is much higher than the model fitting R2 of our LME model. However, the model CV R2

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decreased to 0.64 for GWR model, indicating substantial over-fitting problem.

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Validation of historical PM2.5 estimates

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Figure 3 summarizes the evaluations of historical PM2.5 estimates at daily and monthly levels. We

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required at least 20 daily PM2.5 estimates to calculate the monthly PM2.5. Compared to the accuracy of

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daily PM2.5 estimates in the modeling year (CV R2=0.71), the historical estimates demonstrate poorer

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accuracy (R2=0.60, N=44,884) at daily level. However, when aggregating the daily estimates to

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monthly mean values, the accuracy improved greatly. The evaluation R2 at monthly level is 0.71

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(N=1,545). The poor historical daily PM2.5 estimates were probably due to the strong model

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assumption that the relationships (i.e., month- and province-specific random effects) between daily

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PM2.5 and visibility derived from the 2014 model remained constant for the same month and province

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in each year. Nonetheless, the accuracy of historical monthly mean PM2.5 estimates from 2004 to 2013

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is satisfactory. These results are consistent with our previous satellite AOD-PM2.5 modeling in China

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concentration from 2004-2012, we found poor performance for daily level estimates (R2 = 0.41) but

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fairly accurate performance for monthly mean estimates (R2 = 0.73) 23. Due to the lack of ground

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PM2.5 observations before 2004, we cannot assess the historical daily and monthly PM2.5 estimates

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before 2004, which is a limitation and uncertainty in this study.

. When using the PM2.5-AOD statistical model developed in 2013 to estimate the historical PM2.5

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Figure 3. Evaluation of historical PM2.5 estimates at daily (A) and monthly (B) levels from 2004 to 2013.

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Spatial variations of model estimated PM2.5 concentrations

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Figure 4 presents the spatial distributions of PM2.5 concentrations for different periods. For each period,

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we required at least 60% of days with available PM2.5 data to calculate the average PM2.5 for each site.

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Similar spatial distribution patterns were found across different periods. Higher PM2.5 pollution levels

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always appeared in the north part of North China Plain, Northeast China, Central China, Sichuan

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Basin, and Northwest China. These areas have been suffering relatively high air pollution levels since

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1950s. For example, during 1950s and 1960s, the PM2.5 concentrations have reached 60-80 µg/m3 in

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the north part of North China Plain (Shandong Province and the south part of Hebei Province), which

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were much higher than Level 2 annual PM2.5 standard (35 µg/m3) of China’s National Ambient Air

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Quality Standard (CNAAQ). And the PM2.5 concentrations increased to generally higher than 90

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µg/m3, with maximum values higher than 110 µg/m3 during and after 1970s. The PM2.5 concentrations

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in late 1970s had reached to a similar pollution level as in 2000s except East China (Jiangsu, Anhui,

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and Zhejiang Provinces) and Southeast China (Fujian Province). For Xinjiang Province in Northwest 14


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China, the PM2.5 levels were generally 50-60 µg/m3, which were mainly come from the dust impact of

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Taklimakan Desert and relatively stable over decades.

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Figure 4. Spatial distributions of long-term average PM2.5 estimations for 1957-1964 (A), 1973-1980 (B), 1981-1995 (C), 1996-2005 (D), 2006-2014 (E), and 1957-2014 (F).

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Our findings suggest that air pollution is not a new environmental issue that occurs in the recent 2-3

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decades but a problem existing in a longer time before 1980 28, 29, especially in the areas mentioned

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above. To further confirm these findings, we reviewed the limited papers which studied the air

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pollution in China from1960s-1980s. Although there were no PM2.5 studies in this period, there had

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been a handful of PM10 pollution studies in China, which are summarized in Table 1. Results show

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that these major Chinese cities have suffered severe PM10 pollution since 1970s. Given the fact that the

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proportion of PM2.5 in PM10 ranges from 50% to 85% in Chinese cities30-32, the results from previous

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PM10 studies suggested that Chinese cities have suffered severe PM2.5 pollutions since 1970s.

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Table 1. Ground measured PM10 concentrations in several major cities during 1970s City Beijing

33, 34

Tianjin 35 Shenyang

33

Harbin 36

37

Chongqing

Shanghai 33, 38

PM10 concentrations (µg/m3) a

Year

Site

1974-1976

Shijingshan

1,860

1979

Urban area

510

1973-1974

/

1,480

1979

Urban area

760

1978-1979

Daoli

1,605

948

Daowai

2,127

1,598

Nangang

1,811

1,341

Reference site

285

182

Winter

Spring

Summer

Autumn

Annual 600

1,010

640

670

950

1977

Urban center

510

Jiulongpo

680

1973-1981

Urban area

300-450

1978-1981

Suburban area

140-270

1979

Urban area

350

Lanzhou 33

1979

Urban area

1,290

Dukou (now called Panzhihua) 39

1979-1980

Nongnongping

950

1,150

300

520

730

Bingcaogang

540

370

60

150

280

Renhe

190

220

50

70

130

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The East China seems to be the area which experienced the biggest drop in air quality in China. The

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PM2.5 concentrations in East China were generally lower than 25 µg/m3 during 1950s and 1960s and

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increased to 30-50 µg/m3 in around 1995, and increased to greater than 60 µg/m3 in around 2005. The

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rapid social economic development with a lack of effective environmental regulation after the reform

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and opening started from late 1970s was the major driver for the degradation of PM2.5 concentrations

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in East China. Shanghai City is one exception in East China. The PM2.5 pollution level had reached

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60-70 µg/m3 in 1970s, which was consistent with previous studies shown in Table 1. This is probably

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because Shanghai had been a highly industrialized city before 1980s. Tibet and Hainan Provinces were

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always the cleanest areas in China, and the pollution levels were approximately 15-25 µg/m3 over the

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past decades. Taiwan, Yunnan, and Inner Mongolia were also relatively clean areas, where PM2.5 16


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concentrations were generally 30-40 µg/m3 over the past decades.

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Historical trends of estimated PM2.5 concentrations

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We further quantitatively analyzed the temporal trends of PM2.5 concentrations from 1957 to 2014

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using the monthly PM2.5 anomaly time series. Figure S2 (SI) shows the site-specific annual trends of

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visibility-derived PM2.5 concentrations. Most of sites had the increasing trends during the whole

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studied periods, especially sites in Jiangsu, Anhui, Henan, and Hubei Provinces. The highest

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site-specific trends in these provinces were greater than 1.2 µg/m3/yr, indicating that the PM2.5

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concentrations have increased by more than 60 µg/m3 for those sites during the past 50 years. There

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were also some sites presenting decreasing trends (e.g., sites in Tibet and Qinghai Provinces, and north

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part of Inner Mongolia).

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Figure 5 and Table 2 summarize the overall PM2.5 trends for entire China and four major regions, i.e.,

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Jing-Jin-Ji region (JJJ, including Hebei Province, Tianjin City, and Beijing City), the Yangtze River

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Delta (YRD, including Jiangsu Province, Zhejiang Province, and Shanghai City), and the Pearl River

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Delta (PRD) in Guangdong Province. Overall, the entire China and all three regions experienced

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significant increasing trends from 1957 to 2014. The overall trends for entire China, JJJ, YRD, and

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PRD were 0.19, 0.36, 0.68, and 0.32 µg/m3/yr.

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Figure 5. Time series of visbility-derived monthly PM2.5 anomaly (µg/m3) for entire China (A), Jing-Jin-Ji region (B), the Yangtze River Delta (C), and the Pearl River Delta (D). Table 2. Historical trends and 95% confidence intervals (CI) of estimated PM2.5 concentrations for entire China and Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta Regions. Period

Trend

Entire China 3

1957-2014

0.19

0.36

0.68

0.32

(0.18, 0.20)

(0.30, 0.41)

(0.65, 0.71)

(0.31, 0.34)

Significance

P

Visibility-based PM2.5 concentrations in China - ACS Publications

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