[Source Apportionment of PM2.5 Based on Hybrid Chemical Transport and Receptor Model in Chongqing].

In order to further improve the accuracy of fine particulate matter (PM2.5) source apportionment results, a hybrid source apportionment approach (CTM-RM) combining the capabilities of a receptor model (RM) and chemical transport model (CTM) was developed. The CTM-RM method was evaluated and applied according to a typical PM2.5 pollution process from January 21 to 27, 2019 in Chongqing. The average value of square prediction error based on CTM-RM was 84.58% lower than that of CAMx/PSAT during the campaign. Compared with that of CAMx/PSAT, the fractional error of PM2.5 and its chemical component concentrations decreased by 15.69%-92.86%. Furthermore, the temporal and spatial variations in PM2.5 source impacts could be obtained using the CTM-RM method in Chongqing. The average adjustment factor (R) values were 1.39±0.38 (agriculture sources), 1.54±0.48 (industrial sources), 1.01±0.13 (power sources), 1.02±0.58 (residential sources), 0.86±0.59 (transportation sources), and 0.58±0.67 (other sources) in the main urban areas of Chongqing. Additionally, the cumulative distribution functions of R were found to be distinct among the six sources. The residential and industrial sources were the main sources of PM2.5, with contributions of 46.23% and 28.23%, respectively. In contrast to that of the other sources, the transportation source impacts of PM2.5 (8.62%) increased significantly from the clear period to pollution period (P<0.001), indicating that the increase in PM2.5 concentrations was mainly driven by vehicular emissions during the pollution period in the main urban areas of Chongqing. The fitting functions between the initial simulated concentrations and R values of each source in the main urban areas of Chongqing could be used to evaluate PM2.5 concentrations at 47 air quality monitoring stations in Chongqing, and the correlation between the refined simulated concentrations and measured concentration of PM2.5 was significant (r=0.82, P<0.001). Compared with that during the clear period, the increases in the percentages of industrial source impacts of PM2.5 in Northeast Chongqing and residential source impacts of PM2.5 in Southeast Chongqing were 17.20% and 9.15% higher, respectively, than that in other areas during the pollution period. By contrast, the increasing percentage of transportation source impacts of PM2.5 in the main urban areas of Chongqing (66.39%) and Western Chongqing (84.16%) from the clear period to the pollution period were higher than that in other areas. The results of CTM-RM on January 26 indicated that the residential source impacts in Northeast Chongqing (64.56%) were higher than those in other areas, and the industry source impacts of PM2.5 were primarily observed in the main urban areas of Chongqing and Western Chongqing, with contributions of 25.26% and 21.20%, respectively.

Natural product myricetin is a pan-KDM4 inhibitor which with poly lactic-co-glycolic acid formulation effectively targets castration-resistant prostate cancer.

BACKGROUND: Castration-resistant prostate cancer (CRPC) with sustained androgen receptor (AR) signaling remains a critical clinical challenge, despite androgen depletion therapy. The Jumonji C-containing histone lysine demethylase family 4 (KDM4) members, KDM4A‒KDM4C, serve as critical coactivators of AR to promote tumor growth in prostate cancer and are candidate therapeutic targets to overcome AR mutations/alterations-mediated resistance in CRPC. METHODS: In this study, using a structure-based approach, we identified a natural product, myricetin, able to block the demethylation of histone 3 lysine 9 trimethylation by KDM4 members and evaluated its effects on CRPC. A structure-based screening was employed to search for a natural product that inhibited KDM4B. Inhibition kinetics of myricetin was determined. The cytotoxic effect of myricetin on various prostate cancer cells was evaluated. The combined effect of myricetin with enzalutamide, a second-generation AR inhibitor toward C4-2B, a CRPC cell line, was assessed. To improve bioavailability, myricetin encapsulated by poly lactic-co-glycolic acid (PLGA), the US food and drug administration (FDA)-approved material as drug carriers, was synthesized and its antitumor activity alone or with enzalutamide was evaluated using in vivo C4-2B xenografts. RESULTS: Myricetin was identified as a potent α-ketoglutarate-type inhibitor that blocks the demethylation activity by KDM4s and significantly reduced the proliferation of both androgen-dependent (LNCaP) and androgen-independent CRPC (CWR22Rv1 and C4-2B). A synergistic cytotoxic effect toward C4-2B was detected for the combination of myricetin and enzalutamide. PLGA-myricetin, enzalutamide, and the combined treatment showed significantly greater antitumor activity than that of the control group in the C4-2B xenograft model. Tumor growth was significantly lower for the combination treatment than for enzalutamide or myricetin treatment alone. CONCLUSIONS: These results suggest that myricetin is a pan-KDM4 inhibitor and exhibited potent cell cytotoxicity toward CRPC cells. Importantly, the combination of PLGA-encapsulated myricetin with enzalutamide is potentially effective for CRPC.

[Speciated Emission Inventory of VOCs from Industrial Sources and Their Ozone Formation Potential in Chongqing].

Based on the basic information of the Second National Pollution Source Census and the VOCs source profiles of industrial industries, we established the speciated emission inventory of major industrial sources in Chongqing in 2017, estimated their ozone formation potential (OFP), and identified the key control species of industrial VOCs and their sources. The results showed that the total VOCs emission from industrial sources and their OFPs were 144.12 kt and 477.34 kt, respectively. Automobile manufacturing, equipment manufacturing, plastic manufacturing, and chemical raw materials and chemical products were all industries that contributed significantly to VOCs emissions and OFP, with VOCs emissions of 37.18, 33.09, 19.47, and 18.14 kt and OFP of 191.43, 153.69, 27.21, and 57.51 kt, respectively. Aromatics were the components with the largest contribution to VOCs emissions and OFP, accounting for 62.55% of the total VOCs emissions and 82.15% of the total OFP, mainly from metal surface coating and petrochemical industries. The major reactive species of industrial source VOCs were m/p-xylene, toluene, ethylbenzene, o-xylene, and propylene, with OFP of 130.47, 103.37, 46.37, 42.83, and 28.26 kt, respectively, cumulatively accounting for 71.11% of the total OFP. In terms of spatial distribution, the emission intensity of VOCs and O3 pollution degree in all districts and counties of Chongqing were relatively consistent; the high value points of VOCs emissions and OFP were mainly distributed in the main urban area and the western area, and the sources of VOCs emission in the main urban area and western area were mainly in metal surface coating and the petrochemical industry, respectively.

[Pollution Characteristics and Source Apportionment of Atmospheric VOCs During Ozone Pollution Period in the Main Urban Area of Chongqing].

In late August 2020, a period of O3 pollution occurred in the main urban area of Chongqing and lasted for approximately 2 weeks (till early September). Ambient air samples, collected using Summa Canisters and DNPH sampling columns at three observation sites in the main urban area, were used to study the composition, photochemical reaction activity, and source apportionment of volatile organic compounds (VOCs) during the period of O3 pollution. The results showed that the mean volume fraction of TVOCs in the main urban area of Chongqing during the observation period was 45.08×10-9, and the components were ranked by volume fraction in the following order:OVOCs, alkanes, halohydrocarbons, alkenes, aromatics, and alkynes. Formaldehyde, ethylene, and acetone made up the higher volume fraction of VOCs, together accounting for more than 30% of TVOCs. OVOCs and alkenes contributed more to · OH loss rate (Li·OH) and ozone formation potential (OFP) and were the key VOCs components for ozone generation. The main active species in the OVOCs component were formaldehyde, acetaldehyde, and acrolein; the main active species in the alkene component were isoprene, ethylene, and n-butene. The ratio of xylene to ethylbenzene in VOCs was low, and they showed a significant correlation, indicating that the VOCs air mass in the main urban area was highly aging and affected by long-distance transmission from other areas. The source apportionment results of the PMF model showed five main sources of VOCs, namely secondary generation (27.67%), vehicle exhaust (26.56%), industrial emission (17.86%), plant (14.51%), and fossil fuel combustion (13.4%).

[Carbon source apportionment of PM2.5 in Chongqing based on local carbon profiles].

PM2.5 was sampled from commercial, industrial and residential areas in Chongqing urban city from 2nd May to 10th May 2012 in order to find out characteristics and sources of carbon in PM2.5. Eight kinds of carbons were analyzed by the TOR method. Characteristics of carbon pollution in PM2.5 from three kinds of functional areas and six kinds of sources, including coal-combustion, exhausts (vehicle, boat and construction machine), biomass burning, cooking smoke, were analyzed. Based on carbon source profiles, local indicating components of carbon sources in PM2.5 were obtained used the chemical mass balance (CMB) model. Contribution rate of different sources to PM2.5 carbon were parsed out by factor analysis. The results showed the OC/EC of coal-combustion, vehicle exhausts, boat exhausts, construction machine exhausts, biomass burning and cooking smoke were 6.3, 3.0, 1.9, 1.4, 12.7 and 31.3, respectively. High loads of EC2 and EC3 indicated diesel vehicle exhaust emissions, high loads of OC2, OC3, OC4 and OPC indicated coal-combustion emissions, OC1, OC2, OC3, OC4 and EC1 indicated gasoline vehicle exhaust emissions, OC3 indicated cooking emissions, and OPC indicated biomass burning emissions. OC/PM2.5, EC/PM2.5, secondary organic carbon (SOC)/OC in the commercial area were 17.4%, 6.9% and 40.0%, respectively. OC/PM2.5, EC/PM2.5 and SOC/OC in the industrial area were 15.5%, 6.6% and 37.4%, respectively. OC/PM2.5, EC/PM2.5 and SOC/OC in the residential area were 14.6% 5.6% and 42.8%, respectively. In the industrial area, the main sources of carbon in PM2.5 were coal combustion, gasoline vehicle exhausts and diesel exhaust. In the commercial area, the main sources of carbon were gasoline vehicle exhausts, diesel exhausts and cooking. In the residential area, the main sources of carbon were gasoline vehicle exhausts, cooking smoke and diesel exhausts.