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Abstract: With the new air quality standards forthcoming in China, the Pearl River Delta region is facing new challenges to achieve its air quality goal. The success of the emission reduction measures introduced by local authorities in the run-up to the Guangzhou Asian Games demonstrated that the Pearl River Delta air quality can be improved by introducing integrated emission reduction measures. This paper combines observation data, emission reduction measures, and air quality simulations that were applied during the Asian Games (12–27 November 2010) to analyze the relationship between emissions and concentrations of pollutants in Guangzhou. The Asian Games abatement strategy totally reduced emissions of 41.1% SO2, 41.9% NO x , 26.5% PM10, 25.8% PM2.5 and 39.7% VOC. The concentrations of SO2, NO2, PM10 and PM2.5 were reduced by 66.8%, 51.3%, 21.5% and 17.1%, respectively. In Guangzhou, the main challenge to be overcome with the new air quality daily requirements is mostly for NO2, PM2.5, and hourly ozone maxima. If pollutants maintain the same concentrations before and after the Asian Games, there will be 47.4% and 31.6% non-attainment days for NO2 and PM2.5 respectively as a period average. Although PM10 concentration can meet the daily limits (150 μg m−3), it is quite difficult to meet the annual limit value (70 μg m−3). One important implication is that the long-term, step-by-step integrated measures of the past six years work better than the strict, intensive, short-term measures on SO2, NO2 and VOC control. Dust control by limiting construction sites and watering the roads can further reduce 12.8% of the PM10 concentration. However, to reduce ambient PM2.5, the abatement strategy should be more complex and extensive. On the contrary, ozone pollution was not improved during the Asian Games, indicating that alleviation strategies should be improved by scientific studies to determine the appropriate control ratio of NO2 and VOC in the Pearl River Delta region. [Copyright &y& Elsevier]

Abstract: The Guangzhou government adopted many vehicle emission control policies and strategies during the five-year preparation (2005–2009) to host the 2010 Asian Games. This study established a multi-year emission inventory for vehicles in Guangzhou during 2005–2009 and estimated the uncertainty in total vehicle emissions by taking the assumed uncertainties in fleet-average emission factors and annual mileage into account. In 2009, the estimated total vehicle emissions in Guangzhou were 313 000 (242 000–387 000) tons of CO, 60 900 (54 000–70 200) tons of THC, 65 600 (56 800–74 100) tons of NO x and 2740 (2100–3400) tons of PM10. Vehicle emissions within the urban area of Guangzhou were estimated to be responsible for ∼40% of total gaseous pollutants and ∼25% of total PM10 in the entire city. Although vehicle use intensity increased rapidly in Guangzhou during 2005–2009, vehicle emissions were estimated to have been reduced by 12% for CO, 21% for THC and 20% for PM10 relative to those in 2005. NO x emissions were estimated to have remained almost constant during this period. Compared to the “without control” scenario, 19% (15%–23%) of CO, 20% (18%–23%) of THC, 9% (8%–10%) of NO x and 16% (12%–20%) of PM10 were estimated to have been mitigated from a combination of the implementation of Euro III standards for light-duty vehicles (LDVs) and heavy-duty diesel vehicles and improvement of fuel quality. This study also evaluated several enhanced vehicle emission control actions taken recently. For example, the enhanced I/M program for LDVs was estimated to reduce 11% (9%–14%) of CO, 9% (8%–10%) of THC and 2% (2%–3%) of NO x relative to total vehicle emissions in 2009. Total emission reductions by temporary traffic controls for the Asian Games were estimated equivalent to 9% (7%–11%) of CO, 9% (8%–10%) of THC, 5% (5%–6%) of NO x and 10% (8%–13%) of PM10 estimated total vehicle emissions in 2009. Those controls are essential to further vehicle emission mitigation in Guangzhou required by the new National Ambient Air Quality Standards. [Copyright &y& Elsevier]

Motivated by the sustainable development goals, the Chinese government actively explores effective pathways for carbon emissions reduction. The establishment of the carbon trading market is an economic stimulus policy tool employed by China. Carbon price signals and accurate price predictions play important roles for participants in the carbon trading market and for decision-makers in carbon trading policies. Given these considerations, our study aims to propose a novel hybrid model to enhance the accuracy of carbon price prediction. The model consists of two main components: (1) a CEEFD re-decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Fourier Decomposition (EFD); (2) an innovative hybrid model combining Long Short-Term Memory (LSTM) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). The findings demonstrated that the new model exhibits outstanding performance in predicting current carbon market prices in Hubei, Guangzhou, and Shenzhen. As an example, when evaluating the dataset collected from February 2022 to August 2023 in Hubei carbon market, the model achieves impressive performance with four key prediction metrics: Mean Absolute Error (MAE) of 0.089, Mean Squared Error (MSE) of 0.012, Root Mean Square Error (RMSE) of 0.109, and Mean Absolute Percentage Error (MAPE) of 0.191%. Compared to the initial LSTM model, these four key prediction metrics decreased by 78.29%, 96.11%, 80.47%, and 78.31%, respectively. Our research improves carbon price prediction accuracy by effectively capturing the nonlinear features of price volatility. This improvement directly benefits participants in the carbon market and informs government decision-making processes. Additionally, our research provides new insights and methods for further exploration in time series prediction field. [Display omitted] • Applying the novel decomposition method CEEFD to reduce time series complexity. • Proposing a novel hybrid statistical and deep learning model. • Statistical models enhance prediction accuracy by forecasting time series volatility. • Successfully predicting carbon prices across different Chinese markets. [ABSTRACT FROM AUTHOR]

A hybrid method combining observation data analysis and chemical transport model simulation was used in this study to provide the PM 2.5 source apportionment result of Guangzhou. Four main anthropogenic emission sectors in PRD region were taken into consideration, including mobile, power, industrial and residential. The proportions (Ps) of six major components (sulfate, nitrate, ammonium, SOA, POA and EC) in PM 2.5 were acquired by analyzing the daily PM 2.5 monitoring data collected in the year of 2013 at an urban sampling site in Guangzhou. WRF/Chem model was used to get the contribution ratios (CRs) of each emission sector to the concentrations of six related primary pollutants, including SO 2 , NO X , NH 3 , VOCs, POA and EC. Then the CRs of the four sources to Guangzhou's PM 2.5 mass were calculated. It was found that stationary sources (industrial and power) still had the largest contribution (22.2% in dry season, 44.4% in wet season) to PM 2.5 in Guangzhou. Mobile sector was the predominant single contributor, with an average contribution of 20.7% in dry season and 37.4% in wet season. Almost all the PM 2.5 concentration in Guangzhou was caused by the emissions within PRD region in wet season. In dry season, however, the emissions emitted within PRD region and the pollutants transported from the areas north of PRD region both played important roles. [ABSTRACT FROM AUTHOR]