In the last 10 yr, Beijing has made a great effort to improve its air quality. However, it is still suffering from regional coarse particulate matter (PM10) pollution that could be a challenge to t...

In order to determine the pollution sources and human health risks of metal elements in PM_2.5, samples were collected by a large flow particulate matter sampler in the four seasons in 2013, 2015, and 2017 (January, April, July, and October). The mass concentrations of 10 metals (Ti, V, Cr, Mn, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The sources of heavy metals were identified by Unmix, and the potential non-carcinogenic/carcinogenic risk was evaluated. The influences of local and regional sources were also explored during the high-carcinogenic risk period (HCRP). The wind field and 72 h backward trajectories were performed to identify the potential local and regional sources in HCRP. The results showed that the average annual concentrations of PM_2.5 in the urban area of Handan city were 105.14, 91.18, and 65.85 μg/m³ in 2013, 2015, and 2017, respectively. The average daily concentrations of the metals in PM_2.5 in January were higher than that of April, July, and October. The average mass concentrations of the 10 heavy metal elements in PM_2.5 were 698.26, 486.92, and 456.94 ng·m⁻³ in 2013, 2015, and 2017, respectively. The main sources of the metals in PM_2.5 were soil dust sources, vehicular emissions, coal burning, and industrial activities. The carcinogenic risks of Cr and As were above 1 × 10⁻⁶ over the three years. Wind direction analysis showed that the potential local sources were heavy industry enterprises and the economic development zone. The backward trajectory analysis indicated that PM_2.5 long transported from Shandong, Henan, and the surrounding cities of Handan had quite an impact on the heavy metals contained in the atmosphere of the studied area. The health risk assessment results demonstrated that the trend for non-carcinogenic risk declined, and there was no non-carcinogenic risk in 2017. However, the carcinogenic risk levels were high over the three years, particularly in January.

Soil-heavy metals are potentially harmful to the ecosystem and human health. Quantifying heavy metals sources is conducive to pollution control. In this study, 64 surface-soil samples were collected in Handan city. Cr, Mn, Ni, Cu, Zn, Cd and Pb were determined; then, their spatial distribution in the sampling area was drawn by ArcGIS. The pollution index (PI) method, geo-accumulation index (I_geo) method, Nemerow integrated pollution index (NIPI) and pollution load index (PLI) were used to evaluate the pollution level of heavy metals in surface soil; then, an ecological and health risk assessment of soil-heavy metals was carried out. Combined with the spatial distribution, correlation analysis, cluster analysis, PCA and PMF model, the pollution sources of heavy metals in soil were identified and apportioned. The results showed that the average content of Cd was nearly ten times that of the background limit, which was the most serious among the studied metals. In terms of non-carcinogenic risk, Cr had the highest value, followed by Pb. In terms of carcinogenic risk, Cd, Cr, and Ni had an acceptable or tolerable risk. Three pollution sources were identified by cluster analysis and PCA, including traffic sources with Cu, Pb and Cd as main loads, industrial sources with Mn, Cd and Zn as main loads, and natural sources with Cr and Ni as main loads. The PMF model analyzed three main factors: traffic source (17.61%), natural source (28.62%) and industrial source (53.77%). The source categories and the main load elements obtained from the source apportionment results were consistent with the source identification results.

The 14th Five-Year Plan period is a critical period for China to achieve high-quality development. Based on super-efficiency slacks-based measure (SBM) model, grey-related analysis (GRA) and other models, this paper studies the heterogeneity of the coupling relationship among technological innovation, industrial transformation and environmental efficiency in the Huaihai Economic Zone during the period of 2005–2019. In addition, it analyzes the coupling mechanism of single and binary systems to the ternary system, which is of great significance for the collaborative symbiosis among systems. The findings are as follows. 1) The technological innovation, industrial transformation and environmental efficiency (TIE) systems of the Huaihai Economic Zone had significant spatial-temporal heterogeneity. Although their evaluation value fluctuated, the development trends are all positive. Ultimately, technological innovation is characterized by being high in the northeast and low in the southwest around Xuzhou, while other systems are relatively staggered in space. 2) The coupling of TIE systems is in transition, lack of orderly integration and benign interaction. However, the developing trend of interaction is also upward, and a spatial pattern driven by Xuzhou and Linyi as the dual cores has gradually formed. Moreover, the coupling is mostly manifested as outdated technological innovation and industrial transformation. Except for the final coordination of regenerative cities, the other resource types are all in transition. Cities in all traffic locations are still in transition. The overall system interaction of cities on Longhai Line (Lanzhou-Lianyungang Railway) is relatively optimal, and cities on Xinshi Line (Xinxiang-Rizhao Railway) are accelerating toward synergy. 3) The coupling status of TIE systems depends on the development of the single system and the interaction of the binary (2E) system. The coupling is closely related to technological innovation and Technology-Industry system, and is hindered by the inefficient interaction of Technology-Environment system. Specifically, the synergy of regenerative cities is attributed to the advantage of a single system and the effective integration of 2E systems. Beneficial from the advantages of environmental efficiency, the cities on Xinshi Line promote the synergy of the 2E and TIE systems. Therefore, while the Huaihai Economic Zone stimulates the development potential of the single and 2E systems, it is necessary to amplify the superimposition effect of systems in accordance on the basis of resource and location.

Beijing‐Tianjin‐Hebei area is one of the most polluted areas in China. This paper used the Fifth‐Generation Penn State/NCAR Mesoscale Model (MM5) and Model‐3/Community Multiscale Air Quality (CMAQ) modeling system to quantify the source contribution to PM_2.5 in Hebei cities in order to obtain an in‐depth understanding haze process in January and February 2013, using the Multiresolution Emission Inventory for China (MEIC). The result showed that PM_2.5 were mainly originated from the southern Hebei (SHB) with the fractions of 70.8% and 66.4% to Shijiazhuang, 70.6% and 63.9% to Xingtai, and 68.5% and 63.0% to Handan in January and February 2013, respectively. The northern Hebei (NHB) contributed 69.8% and 70.7% to Zhangjiakou, 68.7% and 66.2% to Chengde, and 57.7% and 59.6% to Qinhuangdao in January and February. In Cangzhou, Hengshui, and Langfang, regional joint policy making should be implemented due to the pollution of multiple sources. In Baoding and Tangshan, industrial emissions contributed 38.1% and 41.9% of PM_2.5 to Baoding and 39.8% and 45.8% to Tangshan in January and February, respectively. Industrial and domestic emissions should be controlled in Tangshan and Baoding, especially for industrial emissions of NHB.