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      Responses of surface O3 and PM2.5 trends to changes of anthropogenic emissions in summer over Beijing during 2014–2019: A study based on multiple linear regression and WRF-Chem

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      Science of The Total Environment
      Elsevier BV

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          Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity

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            Is Open Access

            Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter

            Significance Exposure to outdoor concentrations of fine particulate matter is considered a leading global health concern, largely based on estimates of excess deaths using information integrating exposure and risk from several particle sources (outdoor and indoor air pollution and passive/active smoking). Such integration requires strong assumptions about equal toxicity per total inhaled dose. We relax these assumptions to build risk models examining exposure and risk information restricted to cohort studies of outdoor air pollution, now covering much of the global concentration range. Our estimates are severalfold larger than previous calculations, suggesting that outdoor particulate air pollution is an even more important population health risk factor than previously thought.
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              Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization

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                Author and article information

                Journal
                Science of The Total Environment
                Science of The Total Environment
                Elsevier BV
                00489697
                February 2022
                February 2022
                : 807
                : 150792
                Article
                10.1016/j.scitotenv.2021.150792
                34619192
                e8f48724-716e-47df-9c7d-b5a448b27941
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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