PM 102

Objective:To investigate the effects of hesperetin on fine particulate matter (PM(2.5)) induced apoptosis in H9c2 cells and related mechanisms.Methods:H9c2 cells were divided into 4 groups: control group (cells were cultured without intervention), PM(2.5) group (cells were treated with 800 µg/ml PM(2.5)), hesperetin group (H group, cells were treated by 40 µmol/L hesperetin for 1 h at 37 ℃), and hesperetin+PM(2.5) group (H+PM(2.5) group, cells were pretreated with hesperetin before PM(2.5) treatment). Cells were cultured for corresponding interval. Apoptotic cells were detected by Annexin Ⅴ-FITC/PI apoptosis detection kit and Hoechst staining. The intracellular reactive oxygen species (ROS) levels were measured by DCFH-DA Fluorescence Probe and mitochondrial membrane potential (MMP) was detected with JC-1 staining, respectively in these groups. Apoptotic related protein and phosphorylated MAPK expression levels were determined by Western blot.Results:(1) Flow cytometry results showed that the apoptosis rate of PM(2.5) group ((48.94±3.20)%) was significantly higher than that of control group ((8.13±1.40)%,P<0.01), which was significantly reduced in H+PM(2.5) group ((34.80±2.21)%) (P=0.003 2 vs. PM(2.5) group,P<0.01 vs. control group). The number of Hoechst 33258 positive apoptotic cells was distinctly less in H+PM(2.5) group than in PM(2.5) group. (2) The ROS levels was significantly higher in PM(2.5) group ((49.69±5.05)%) than in control group (10.57±1.33)%,P<0.01), which was significantly reduced in H+PM(2.5) group ((35.08±3.90)%) (P=0.000 2 vs. PM(2.5) group,P<0.01 vs. control group). (3) Green fluorescence indicating the JC-1 monomer form, which represented MMP loss of H9c2 cells, was significantly higher in PM(2.5) group ((20.28±4.69)%) than in control group ((10.50±2.72)%,P<0.01), which was significantly decreased in H+PM(2.5) group ((13.41±2.89)%) (P<0.01 vs. PM(2.5) group,P=0.029 4 vs. control group). (4) The expression levels of Bcl-2 protein of H9c2 cells was lower in PM(2.5) group ((76.94±4.52)%) than in control group (100%,P=0.000 9), which was significantly upregulated in H+PM(2.5) group ((92.95±6.82)%) (P=0.027 5 vs. PM(2.5) group,P=0.15 vs. control group). The expression levels of cleaved caspase-3 protein of H9c2 cells was significantly higher in PM(2).5 group ((243.98±17.94)%) than in control group (100%,P=0.000 2), which was significantly downregulated in H+PM(2.5) group ((200.45±4.31)%) (P=0.015 vs. PM(2.5) group,P<0.01 vs. control group). (5) The expression levels of phosphorylated p38 MAPK protein of H9c2 cells was higher in PM(2.5) group ((118.90±4.78)%) than in control group(100%,P=0.002 7), which could be significantly downregulated in H+PM(2.5) group ((103.30±1.27)%) (P=0.01 vs. PM(2.5) group,P=0.05 vs. control group). The expression levels of phosphorylated ERK protein of H9c2 cells was higher in PM(2.5) group ((163.50±4.98)%) than in control group (100%,P<0.01), which was significantly downregulated in H+PM(2.5) group ((139.10±2.72)%) (P=0.001 6 vs. PM(2.5) group,P<0.01 vs. control group).Conclusions:Hesperetin protects H9c2 cells from PM(2.5) stimulation through reducing oxidative stress and protecting mitochondrial function, regulating the expression of apoptotic associated proteins as well as MAPK signal pathway, thus inhibiting H9c2 cells apoptosis.

Background:Much of the current evidence of associations between long-term PM2.5and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3reduction in PM2.5was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979-1983 averages of PM2.5across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.Methods:We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999-2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979-1983 and 1999-2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.Results:A 10 μg/m3decrease in modeled PM2.5based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.Conclusions:Our approach for estimating population- and spatially-representative PM2.5concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.

Tourism is regarded as a major global industry. Given the importance of identifying factors affecting the tourism industry and attracting international tourists, the present ecological study explored the impact of environmental pollution on the number of international tourists arrival using concentrations of PM2.5(particulate matter 2.5 μm or less in size) in a multivariate framework under the context of 190 countries. Using panel data from 190 countries, the author explored the data on the number of international tourists arriving in countries in 2017 extracted from the World Bank (WB) website, and obtained the information about the concentrations of PM2.5from the World Health Organization (WHO) website. Pearson's correlation coefficient and linear regression analysis were used to examine the correlation of the number of tourists with the variables of daily concentrations of PM2.5, societal safety, international conflict, and the relationship of tourist arrival with the studied variables, respectively. The number of countries with low, moderate, and high concentrations of PM2.5in urban areas was 33, 116, and 41, respectively. This numbers for rural areas was 47, 102, and 42 countries, respectively. The mean concentrations of PM2.5in the surveyed countries was 23.90 ± 15.81 and 25.69 ± 16.76 for rural and urban areas, respectively. The estimation results revealed that there was a significant correlation between the number of tourists with the concentrations of PM2.5in the rural areas (p= 0.01). There was also a significant relationship between the human development index (HDI) and the concentration of PM2.5. A significant relationship was observed in the results of univariate linear regression analysis between tourist arrival with rural concentrations of PM2.5(p= 0.02) and societal safety (p= 0.003). After adjusting the effect of societal safety variables, domestic and international conflict, the relationship between tourist arrivals and concentrations of PM2.5inrural area remained significant (p= 0.02). The results imply that by reducing the concentration of PM2.5the positive attitude of tourists for traveling to countries with healthy air can be earned.

The study represents the seasonal characteristics (carbonaceous aerosols and elements) and the contribution of prominent sources of PM2.5and PM10in the high altitude of the eastern Himalaya (Darjeeling) during August 2018-July 2019. Carbonaceous aerosols [organic carbon (OC), elemental carbon (EC), and water soluble organic carbon (WSOC)] and elements (Al, Fe, Ti, Cu, Zn, Mn, Cr, Ni, Mo, Cl, P, S, K, Zr, Pb, Na, Mg, Ca, and B) in PM2.5and PM10were analyzed to estimate their possible sources. The annual concentrations of PM2.5and PM10were computed as 37±12 μg m-3and 58±18 μg m-3, respectively. In the present case, total carbonaceous species in PM2.5and PM10were accounted for 20.6% of PM2.5and 18.6% of PM10, respectively, whereas trace elements in PM2.5and PM10were estimated to be 15% of PM2.5and 12% of PM10, respectively. Monthly and seasonal variations in mass concentrations of carbonaceous aerosols and elements in PM2.5and PM10were also observed during the observational period. In PM2.5, the annual concentrations of POC and SOC were 2.35 ± 1.06 μg m-3(66% of OC) and 1.19±0.57 μg m-3(34% of OC), respectively, whereas annual average POC and SOC concentrations in PM10were 3.18 ± 1.13 μg m-3(63% of OC) and 2.05±0.98 μg m-3(37% of OC), respectively. The seasonal contribution of POC and SOC were ranging from 55 to 77% and 33 to 45% of OC in PM2.5, respectively, whereas in PM10, the seasonal contributions of POC and SOC were ranging from 51 to 73% and 37 to 49% of OC, respectively. The positive relationship between OC & EC and OC & WSOC of PM2.5and PM10during all the seasons (except monsoon in case of PM10) indicates their common sources. The enrichment factors (EFs) and significant positive correlation of Al with othe crustal elements (Fe, Ca, Mg, and Ti) of fine and coarse mode aerosols indicate the influence of mineral dust at Darjeeling. Principal component analysis (PCA) resolved the four common sources (biomass burning + fossil fuel combustion (BB + FFC), crustal/soil dust, vehicular emissions (VE), and industrial emissions (IE)) of PM2.5and PM10in Darjeeling.