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The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China

The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China. Jiang Xue 1 , Shunxi Deng 1 , Ning Liu 1 , Binggang Shen 2. 1 Chang’an University, Xi’an, China 2 Shaanxi Institute of Environmental Sciences and Technology Xi’an, China. Chang’an University.

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The Statistical Distributions of SO 2 , NO 2 and PM 10 Concentrations in Xi’an, China

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  1. The Statistical Distributions of SO2, NO2 and PM10 Concentrations in Xi’an, China Jiang Xue 1, Shunxi Deng 1, Ning Liu 1, Binggang Shen 2 1 Chang’an University, Xi’an, China 2 Shaanxi Institute of Environmental Sciences and Technology Xi’an, China Chang’an University

  2. Xi’an, is one of four world-famous ancient cities

  3. Introduction • In this work, the time series data of three conventional air pollutants concentrations in recent years were taken and analyzed. • The purpose is to determine the best distribution models for SO2, NO2 and PM10 concentrations and to estimate the required emission reduction to meet the ambient air quality standard (AAQS), through fitting the daily average concentration data to the several used commonly distribution functions.

  4. Data sources • The data were taken over a three-year periodfrom 1 January 2006 to 31 December 2008,the time series data of three air pollutants were measured atseven ambient monitoring stationsin Xi’an. • The detailed locations of these stations are shown in Fig.1 Fig1. The locations of the monitoring sites in Xi’an

  5. The variability of daily average concentration of air pollutants with time Table 1 Summary of the basic statistics Fig.2. The variability of daily average concentration for each air pollutant with time. (a) SO2 (b) NO2 (c) PM10, from 1 January 2006 to 31 December 2008. Note: the unit are mg/m3.

  6. The daily average concentrations of three pollutants have strongly seasonal variability from these figures. • Fig.2 also shows the exceedance of three air pollutants, and the probabilities of exceeding the secondary standard of AAQS are 1.09% for SO2, 0.82% for NO2 and 20.73% for PM10. This means that the number of days exceeding the AAQS for three air pollutants in a year are 4, 3 and 76, respectively. • The probability of exceedance for PM10 is significantly higher than SO2 and NO2. • So, PM10 has become a major air pollutant in Xi’an.

  7. Distribution models used in representing air pollutant concentrations • In this study, the following distributions are chosen to fit the concentration data, they areLognormal, Gamma, Inverse Gaussian, Log-logistic, Beta, Pearson 5, Pearson 6, Weibull and Extreme value distributions.

  8. Goodness-of-fit tests • The goodness-of-fit tests are used to determine the most appropriate statistical distribution model of air pollutant concentrations, including KS test, AD test , PCC test and Chi-squared test. • KS test： • AD test： • test：

  9. The identification of the best distribution model Table 2 The results of goodness-of-fit tests Note: The number in parentheses is the results of goodness-of-fit tests; red font corresponding distribution is the best distribution model under the different goodness-of-fit tests.

  10. The most appropriate statistical distribution models for the daily average concentration of SO2, NO2 and PM10 were Pearson 6, Extreme Value and Log-Logistic distributions, respectively (Fig.3). Mean = 0.0514 mg/m3 S.dev = 0.0390 mg/m3 Mean = 0.0416 mg/m3 S.dev = 0.0135 mg/m3 Mean = 0.1268 mg/m3 S.dev = 0.0574 mg/m3 (a) PM10 (a) SO2 (a) NO2 Fig.3. The best distribution models of three air pollutant concentrations: (a) SO2 (b) NO2 (c) PM10.

  11. Parameter estimation • The commonly methods of parameter estimation are the maximum likelihood estimator (MLE), the least square estimator (LSE), the method of quantiles (MoQ) and the method of moments (MoM). MoM is more widely used and MLE provides the best estimate of the parameters (Lynn, D.A., 1974). • In the study, MLE was used, it is defined as:

  12. The estimated values of parameters for the best distribution model of air pollutants are shown in Table 3. Table 3 The estimated values of parameters

  13. Estimating the emission source reduction in Xi’an • After determining the most appropriate distribution model for air pollutant concentrations, the emission source reduction R (%) required to meet the AAQS can be predicted from a rollback equation: • where E{c}s is the expected concentration of distribution when the extreme value equals cs (i.e. the values of the AAQS), E{c} is the mean concentration of the actual distribution and cb is the background concentration.

  14. Table 4 The emission reduction Note: when estimating the emission reduction in this study, cb is neglected in the rollback equation. • Therefore, the emission source reductions of SO2, NO2 and PM10 concentrations to meet the AAQS are -16.7%, 3.8% and 21.1%, respectively. • It means that the annual average SO2 concentration meets to the AAQS without requiring further mitigation and with an environmental capacity of 16.7% in future, while control of PM10 and NO2 emission sources in Xi’an should be increased in order to reduce the concentration and meet the AAQS.

  15. Thank you very much!

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