The impact of emission reduction policies on the results of PM_2.5 emission sources during the 2016 G20 summit: Insights from carbon and nitrogen isotopic signatures

Intensive field observations were conducted on the PM_2.5, gaseous pollutants, δ¹³C, and δ¹⁵N values to evaluate the efficacy of much stricter air quality measures and emergency response strategies implemented in Hangzhou and Ningbo during the 2016 G20 Summit. Our data showed that δ¹³C values of PM_2.5 samples collected in two cities varied from −30.8‰ to −24.2‰, with an average of −25.8‰ ± 1.1‰ during sampling periods. Similar δ¹³C values observed in the two cities indicated that there is no pronounced differences of carbon sources between the Hangzhou and Ningbo. Based on the Bayesian model, results indicated that C₃ plant combustion contributed 47.8% and 47.2% to carbon sources of PM_2.5 in Hangzhou and Ningbo, respectively. δ¹⁵N values of PM_2.5 ranged from 2.0‰ to 13.7‰, with a mean value of 8.5‰ ± 2.5‰ during the whole campaign. Notably, more effective results were observed in reducing NH₃ compared to NO_x in Hangzhou and Ningbo. Additionally, a more reliable source apportionment approach using isotope fractionation effects and a Bayesian model was applied (improved Bayesian model), which reduced the uncertainty in the apportionment of nitrogen sources. The estimated fractionation effects from NH₃ to NH₄⁺ averaged at 16.19‰ ± 3.49‰, and from NO_x to NO₃⁻ averaged at 12.94‰ ± 2.60‰, respectively. Compared to the results of the Bayesian model, the contributions of NO_x from coal combustion (13.8%) and vehicle exhausts (11.6%), and NH₃ from coal combustion (12.1%) and vehicle exhausts (12.5%) by improved Bayesian model were more consistent with the emission inventory. Collective evidence of multiple isotopes indicated that pollution emissions were sensitive in response to strict control measures. Our results also revealed that fossil fuel combustion reduction and regional control policies should be synergistically implemented for better air quality.