The advent of high-resolution minute-level traffic flow data from video surveillance on roads has opened up new opportunities for enhancing the estimation of traffic noise levels. In this study, we propose an innovative method that utilizes time series traffic flow data (TSTFD) to estimate traffic noise levels using a deep learning Convolutional Neural Network (CNN). Unlike traditional traffic flow data, TSTFD offer a unique structure and composition suitable for multidimensional data analysis. Our method was evaluated in a pilot study conducted in Foshan City, China, utilizing traffic flow information obtained from roadside video surveillance systems. Our results indicated that the CNN-based model surpassed traditional data-driven statistical models in estimating traffic noise levels, achieving a reduction in mean squared error (MSE) by 10.16%, mean absolute error (MAE) by 4.48%, and an improvement in the coefficient of determination (R²) by 1.73%. The model demonstrated robust generalization capabilities throughout the test period, exhibiting mean errors ranging from 0.790 to 1.007 dBA. However, the model’s applicability is constrained by the acoustic propagation environment, demonstrating effectiveness on roads with similar surroundings while showing limited applicability to those with different surroundings. Overall, this method is cost-effective and offers enhanced accuracy for the estimation of traffic noise level.

Identifying the emission source contributions to PM2.5 is essential for a sound PM2.5 pollution control policy. In this study, we conduct a comparative analysis of PM2.5 source contributions over the Pearl River Delta (PRD) region of China using two advanced source contribution modeling techniques: Re-sponse Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT). Our compara-tive analyses show that RSM and PSAT can both reasonably predict the contribution of primary PM2.5 emission sources to PM2.5 formation due to its linear nature. For the secondary PM2.5 formed by the nonlinear reactions among PM2.5 precursors, however, our study shows that PSAT appears to have limi-tations in quantifying the nonlinear contribution of PM2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM2.5 precursors (e.g., PM2.5 disbenefits due to local NOx emission reductions in major cities with high NOx emissions). The pilot study case results show that for the ambient PM2.5 in the central cities (Guangzhou, Shenzhen, Foshan, Dongguan, and Zhongshan) of the PRD, the regional source emissions contribute the most by 42-66%; the dust emissions are the top contribution sources (29-34% by RSM and 27-31% by PSAT), and the mobile sources are listed as the secondary contributors accounting for 16-25% by RSM and 19-30% by PSAT among the anthropogenic emission sources. The city-scale cooperation on emission reductions and the enhancement of dust and mobile emission control are recommended to effectively reduce the ambient PM2.5 concentration in the PRD.(C) 2021 Elsevier B.V. All rights reserved.

Quantification of source impacts and contributions is a key element for the design of effective air pollution control policies. In this study, O-3 source impacts and contributions were comprehensively assessed over the Pearl River Delta (PRD) region of China using brute-force method (BFM), response surface modeling with BFM (RSM-BFM) and differential method (RSM-DM) respectively, high-order decoupled direct method (HDDM), and ozone source apportionment technology (OSAT). The multi-modeling comparison results indicated that under typical nonlinear atmospheric conditions during the O-3 formation, BFM, RSM-BFM, and HDDM seemed to be appropriate for assessing the impact of single source emissions; however, the results of HDDM could deviate from those of BFM when the emission reduction ratio was higher than 50 %. Under multi-source control scenarios, the results of source contribution analyses obtained from RSM-DM and OSAT were reasonably well, but the performance of OSAT was limited by its capability in representing the nonlinearity of O-3 response to emission reductions of its precursors, particularly NOx. The results of this pilot study in the PRD showed that the RSM-DM appeared to replicate the nonlinearity of O-3 chemistry reasonably well (e.g., O-3 disbenefits due to local NOx emission reductions in Guangzhou city). Based on the source contribution results, on-road mobile (including both NO(x )and VOC emissions) and industrial process (mainly VOC emissions) sources were identified as two major contribution sectors by both RSM-DM and OSAT, contributing an average of 31.5 % and 11.4 % (estimated by RSM-DM) and 29.2 % and 13.0 % (estimated by OSAT) respectively to O-3 formation in 9 cities of the PRD. Therefore, the reinforced emission reductions on NOx and VOC from on-road mobile and industrial process sources in the central cities (i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan) were suggested to effectively mitigate the ambient O-3 levels in the PRD.

To develop a sound ozone (O3) pollution control strategy, it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O3. Using the "Shunde" city as a pilot summer case study, we apply an innovative response surface modeling (RSM) methodology based on the Community Multi-Scale Air Quality (CMAQ) modeling simulations to identify the O3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O3 impacts of volatile organic compound (VOC) control strategy. Our results show that Shunde is a typical VOC-limited urban O3 polluted city. The "Jiangmen" city, as the main upper wind area during July 2014, its VOCs and nitrogen oxides (NOx) emissions make up the largest contribution (9.06%). On the contrary, the contribution from local (Shunde) emission is lowest (6.35%) among the seven neighbor regions. The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde. The results of dynamic source contribution analysis further show that the local NOx control could slightly increase the ground O3 under low (10.00%) and medium (40.00%) reduction ratios, while it could start to turn positive to decrease ground O3 under the high NOx abatement ratio (75.00%). The real-time assessment of O3 impacts from VOCs control strategies in Pearl River Delta (PRD) shows that the joint regional VOCs emission control policy will effectively reduce the ground O3 concentration in Shunde.

研发了特种污染物排放源强反演及可视化分析工具,集成利用了WRF气象预报结果及HYSPLIT前向轨迹规划外场监测点,调用CALPUFF模型基于监测数据和对应气象条件快速反算源强并模拟得到排放影响范围,并使用开源GIS平台进行可视化分析.同时,选取大气背景浓度极低的活性气态汞(Hg2+)为目标,以李坑垃圾焚烧厂不稳定Hg2+排放为监测主体.结果表明,基于下风向监测数据反演的源强与实测的烟气Hg2+含量有着较好的一致性(R2 =0.801).该工具可作为特种大气污染事故应急辅助决策新手段,提供包含非稳态排放源强、污染扩散影响范围等信息在内的关键依据.

Performance of CMAQ-Hg is better using Model-driven BCs than default BC. Model-VAT provides a better user experience to convert Model-driven BCs. Model-VAT is designed to efficiently access and analyze the results of multi-models. Atmospheric models are essential tools to study the behavior of air pollutants. To interpret the complicated atmospheric model simulations, a new-generation Model Visualization and Analysis Tool (Model-VAT) has been developed for scientists to analyze the model data and visualize the simulation results. The Model-VAT incorporates analytic functions of conventional tools and enhanced capabilities in flexibly accessing, analyzing, and comparing simulated results from multi-scale models with different map projections and grid resolutions. The performance of the Model-VAT is demonstrated by a case study of investigating the influence of boundary conditions (BCs) on the ambient Hg formation and transport simulated by the CMAQ model over the Pearl River Delta (PRD) region. The alternative BC options are taken from (1) default time-independent profiles, (2) outputs from a CMAQ simulation of a larger nesting domain, and (3) concentration files from GEOS-Chem (re-gridded and re-projected using the Model-VAT). The three BC inputs and simulated ambient concentrations and deposition were compared using the Model-VAT. The results show that the model simulations based on the static BCs (default profile) underestimates the Hg concentrations by ~6.5%, dry depositions by ~9.4%, and wet depositions by ~43.2% compared to those of the model-derived (e.g. GEOS-Chem or nesting CMAQ) BCs. This study highlights the importance of model nesting approach and demonstrates that the innovative functions of Model-VAT enhances the efficiency of analyzing and comparing the model results from various atmospheric model simulations.