4. Results and discussions4.1. Regi

4. Results and discussions

4.1. Regional source contributions
In this paper, the regional source contributions refer to the contributions from the emissions outside the objective city, and the local source contributions refer to the contributions from the anthropogenic emissions source within the objective city. Given the multiple emission reduction scenarios conducted in this study, we can use two methods to estimate the regional source contributions to each city. One is by zeroing out the all the anthropogenic emis- sions in the objective city, then the predicted concentrations can be seen as the regional source contributions, as expressed in the equation (1). The other is using the base case predictions minus the local source contributions. The local source contributions are calculated by adding up the contributions of each local emission sector, which is estimated by zeroing out the emissions from each sector and then calculating their differences to the base case pre- dictions, as shown in equation (2). where CR,i is the regional source contributions to city i; CBase is the base case predictions; Ci-0 is the predictions by the scenario of zeroing out all the anthropogenic emissions in the city i; Ci,k-0 is the predictions by the scenario of zeroing out the emissions from the sector k in city i; n is the number of the sectors, which is 5 in this study (PO, IN, DO, TR, and AG). All C can be either concentrations or the percentage to the base case concentrations).
Table 4 presents the regional source contributions to the urban average PM2.5 concentrations in Shijiazhuang, Xingtai and Handan city, calculated by the above two equations. It can be seen that the two methods give quite similar estimations, e.g., the regional contributions in January in Shijiazhuang are both 33.0% by the equations (1) and (2), and in summer, the difference between the two methods is 2.4% (23.9% vs. 21.5%) for Shijiazhuang, which may partially attribute to the more active atmospheric chemistry between the aerosol and its gaseous precursors that induces more obvious non-linear relationship between the emissions and atmo- spheric concentrations. Nevertheless, the average regional contri- butions in January and July are 33.0% and 22.9% in Shijiazhuang, 51.1% and 42.1% in Xingtai, and 47.9% and 32.9% in Handan, respectively. The annual average, as an approximate estimation by averaging the two months, is 27.9% in Shijiazhuang, 46.6% in Xingtai, and 40.4% in Handan. The regional contribution in Shijiazhuang is the smallest out of the three cities, which may attribute to its more stable atmosphere and lower PBL, according to Wang et al. (2014b). This result is consistent with the source apportionment results published by the Hebei EPD (23e30% of regional contributions, http://www.hebhb.gov.cn/hjzw/hbhbzxd/ dq/201409/t20140901_43629.html). The regional source contribu- tion of Xingtai is as high as 46.6%, which can be attributed to its location (between the two cities with large emission density, e.g., Shijiazhuang and Handan rank 2nd and 3rd in GDP in Hebei, and Xingtai ranks 7th (HBBS, 2014)), smaller land area, and different meteorological conditions comparing to Shijiazhuang, i.e., the at- mosphere in Xingtai are not as stable as in Shijiazhuang (Wang et al., 2014b), and the prevailing southeast or northwest wind may blow in the pollutants from either Handan or Shijiazhuang. Averagely, the regional contribution to PM2.5 in Handan is 40.4%, which is also a significant number that should be considered in future air pollution control in Handan. One important point that should be noted is that overestimation may exist in the above regional contribution analysis, due to the lack of dust emissions, which contribute more from the local than from the regional transport. According to the source apportionment results published by Hebei EPD (http://www.pm25.com/news/496.html) by receptor models, the contributions of the local dust emissions to PM2.5 in Shijiazhuang and Handan, out of the total local sources, are 22.5% and 18.4%, respectively (HEBEU, 2015; http://www.pm25.com/ news/496.html). Therefore, we can roughly estimate the underes- timation of the regional contributions due to the lack of local dust emissions, which are 4.8% and 4.8% for Shijiazhuang and Handan, respectively (e.g., for Shijiazhuang, the revised regional contribu- tion is 27.9%/((1e27.9%)/(1e22.5%) þ 27.9%) ¼ 23.1%). As for Xingtai, the underestimation might be in the range of 5.0e6.2%, if we consider its larger regional contributions (46.6%) and suppose its local dust contribution in the range of those for Shijiazhuang and Handan.

4.2. Local sector source contributions

The local sector contributions in each city are calculated by the zeroing out the emissions from each sector in each season. The results are summarized in Table 5, including the average contri- butions and the percentage of each sector contribution within the

Kết quả (Việt) 1:

4. kết quả và thảo luận4.1. khu vực nguồn đóng gópTrong bài báo này, những đóng góp của khu vực nguồn tham khảo các khoản đóng góp từ lượng khí thải ra ngoài thành phố mục tiêu, và đóng góp nguồn địa phương tham khảo các khoản đóng góp từ các nguồn phát thải anthropogenic trong mục tiêu thành phố. Đưa ra các kịch bản giảm phát thải nhiều tiến hành trong nghiên cứu này, chúng tôi có thể sử dụng hai phương pháp ước tính đóng góp nguồn khu vực để mỗi thành phố. Một là do zeroing trong các tất cả các anthropogenic emis-sions ở thành phố mục tiêu, sau đó nồng độ dự đoán có thể được xem như là sự đóng góp của khu vực nguồn, như thể hiện trong phương trình (1). Khác là sử dụng các cơ sở trường hợp dự đoán trừ đi các khoản đóng góp nguồn địa phương. Những đóng góp của địa phương nguồn được tính bằng cách thêm các sự đóng góp của từng lĩnh vực địa phương phát thải, ước tính bởi zeroing ra lượng khí thải từ từng lĩnh vực và sau đó tính toán sự khác biệt của họ để dictions trước khi trường hợp cơ sở như thể hiện trong phương trình (2). CR, tôi đâu đóng góp nguồn khu vực thành phố i; CBase là những dự đoán trường hợp cơ sở; Ci-0 là dự đoán của các kịch bản của zeroing trong tất cả các phát thải anthropogenic trong thành phố tôi; CI, k-0 là dự đoán của các kịch bản của zeroing ra lượng khí thải từ k khu vực trong thành phố tôi; n là số lượng các lĩnh vực, đó là 5 trong nghiên cứu này (PO, IN, DO, TR và AG). Tất cả C có thể có nồng độ hoặc tỷ lệ phần trăm đến nồng độ trường hợp cơ sở).Table 4 presents the regional source contributions to the urban average PM2.5 concentrations in Shijiazhuang, Xingtai and Handan city, calculated by the above two equations. It can be seen that the two methods give quite similar estimations, e.g., the regional contributions in January in Shijiazhuang are both 33.0% by the equations (1) and (2), and in summer, the difference between the two methods is 2.4% (23.9% vs. 21.5%) for Shijiazhuang, which may partially attribute to the more active atmospheric chemistry between the aerosol and its gaseous precursors that induces more obvious non-linear relationship between the emissions and atmo- spheric concentrations. Nevertheless, the average regional contri- butions in January and July are 33.0% and 22.9% in Shijiazhuang, 51.1% and 42.1% in Xingtai, and 47.9% and 32.9% in Handan, respectively. The annual average, as an approximate estimation by averaging the two months, is 27.9% in Shijiazhuang, 46.6% in Xingtai, and 40.4% in Handan. The regional contribution in Shijiazhuang is the smallest out of the three cities, which may attribute to its more stable atmosphere and lower PBL, according to Wang et al. (2014b). This result is consistent with the source apportionment results published by the Hebei EPD (23e30% of regional contributions, http://www.hebhb.gov.cn/hjzw/hbhbzxd/ dq/201409/t20140901_43629.html). The regional source contribu- tion of Xingtai is as high as 46.6%, which can be attributed to its location (between the two cities with large emission density, e.g., Shijiazhuang and Handan rank 2nd and 3rd in GDP in Hebei, and Xingtai ranks 7th (HBBS, 2014)), smaller land area, and different meteorological conditions comparing to Shijiazhuang, i.e., the at- mosphere in Xingtai are not as stable as in Shijiazhuang (Wang et al., 2014b), and the prevailing southeast or northwest wind may blow in the pollutants from either Handan or Shijiazhuang. Averagely, the regional contribution to PM2.5 in Handan is 40.4%, which is also a significant number that should be considered in future air pollution control in Handan. One important point that should be noted is that overestimation may exist in the above regional contribution analysis, due to the lack of dust emissions, which contribute more from the local than from the regional transport. According to the source apportionment results published by Hebei EPD (http://www.pm25.com/news/496.html) by receptor models, the contributions of the local dust emissions to PM2.5 in Shijiazhuang and Handan, out of the total local sources, are 22.5% and 18.4%, respectively (HEBEU, 2015; http://www.pm25.com/ news/496.html). Therefore, we can roughly estimate the underes- timation of the regional contributions due to the lack of local dust emissions, which are 4.8% and 4.8% for Shijiazhuang and Handan, respectively (e.g., for Shijiazhuang, the revised regional contribu- tion is 27.9%/((1e27.9%)/(1e22.5%) þ 27.9%) ¼ 23.1%). As for Xingtai, the underestimation might be in the range of 5.0e6.2%, if we consider its larger regional contributions (46.6%) and suppose its local dust contribution in the range of those for Shijiazhuang and Handan.4.2. địa phương khu vực nguồn đóng gópNhững đóng góp của khu vực kinh tế địa phương ở mỗi thành phố được tính bằng zeroing ra lượng khí thải từ mỗi khu vực kinh tế trong mỗi mùa. Các kết quả được tóm tắt trong bảng 5, bao gồm cả contri-butions trung bình và tỷ lệ phần trăm của mỗi ngành đóng góp trong các