scholarly journals Reduced-complexity air quality intervention modelling over China: development of the InMAPv1.6.1-China and comparison with the CMAQv5.2 model

2021 ◽  
Author(s):  
Ruili Wu ◽  
Christopher W. Tessum ◽  
Yang Zhang ◽  
Chaopeng Hong ◽  
Yixuan Zheng ◽  
...  
Keyword(s):  
Air Quality ◽  
Control Policy ◽  
Premature Mortality ◽  
The United States ◽  
Assessment Model ◽  
Health Impacts ◽  
Emission Sources ◽  
Air Quality Model ◽  
Quality Model ◽  
Reduced Complexity

Abstract. This paper presents the first development and evaluation of the reduced-complexity air quality model for China. In this study, a reduced-complexity air quality intervention model over China (InMAPv1.6.1-China, hereafter, InMAP-China) is developed by linking a regional air quality model, a reduced-complexity air quality model, an emission inventory database for China, and a health impact assessment model to rapidly estimate the air quality and health impacts of emission sources in China. The modelling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional CMAQ simulations and ground-based observations. We found that InMAP-China satisfactorily predicted total PM2.5 concentrations in terms of statistical performance. Compared with the observed PM2.5 concentrations, the mean bias (MB), normalized mean bias (NMB), and correlations of the total PM2.5 concentrations are −8.1 μg/m3, −18 %, and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the United States. The underestimation of total PM2.5 concentrations was mainly caused by its composition, primary PM2.5. In terms of the ability to quantify source contributions of PM2.5 concentrations, InMAP-China presents similar results in comparison with those based on the CMAQ model, the difference is mainly caused by the different mechanism and the treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM2.5-related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 25 ten thousand deaths lower than that estimated based on CMAQ simulations as a result of underestimation of PM2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China, provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy, and to quantify the source contribution attributable to many emission sources.

scholarly journals Reduced-complexity air quality intervention modeling over China: the development of InMAPv1.6.1-China and a comparison with CMAQv5.2

2021 ◽  
Vol 14 (12) ◽  
pp. 7621-7638
Author(s):  
Ruili Wu ◽  
Christopher W. Tessum ◽  
Yang Zhang ◽  
Chaopeng Hong ◽  
Yixuan Zheng ◽  
...  
Keyword(s):  
Air Quality ◽  
Control Policy ◽  
Premature Mortality ◽  
Health Impact ◽  
Assessment Model ◽  
Health Impacts ◽  
Emission Sources ◽  
Air Quality Model ◽  
Quality Model ◽  
Reduced Complexity

Abstract. This paper presents the first development and evaluation of a reduced-complexity air quality model for China. In this study, the reduced-complexity Intervention Model for Air Pollution over China (InMAP-China) is developed by linking a regional air quality model, a reduced-complexity air quality model, an emission inventory database for China, and a health impact assessment model to rapidly estimate the air quality and health impacts of emission sources in China. The modeling system is applied over mainland China for 2017 under various emission scenarios. A comprehensive model evaluation is conducted by comparison against conventional Community Multiscale Air Quality (CMAQ) modeling system simulations and ground-based observations. We found that InMAP-China satisfactorily predicted total PM2.5 concentrations in terms of statistical performance. Compared with the observed PM2.5 concentrations, the mean bias (MB), normalized mean bias (NMB) and correlations of the total PM2.5 concentrations are −8.1 µg m−3, −18 % and 0.6, respectively. The statistical performance is considered to be satisfactory for a reduced-complexity air quality model and remains consistent with that evaluated in the USA. The underestimation of total PM2.5 concentrations was mainly caused by its composition, primary PM2.5. In terms of the ability to quantify source contributions of PM2.5 concentrations, InMAP-China presents similar results to those based on the CMAQ model, with variation mainly caused by the different treatment of secondary inorganic aerosols in the two models. Focusing on the health impacts, the annual PM2.5-related premature mortality estimated using InMAP-China in 2017 was 1.92 million, which was 250 000 deaths lower than estimated based on CMAQ simulations as a result of the underestimation of PM2.5 concentrations. This work presents a version of the reduced-complexity air quality model over China that provides a powerful tool to rapidly assess the air quality and health impacts associated with control policy and to quantify the source contribution attributable to many emission sources.

scholarly journals Global, High-Resolution, Reduced-Complexity Air Quality Modeling Using InMAP(Intervention Model for Air Pollution)

2021 ◽  
Author(s):  
Sumil Thakrar ◽  
Christopher Tessum ◽  
Joshua Apte ◽  
Srinidhi Balasubramanian ◽  
Dylan B Millet ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
The United States ◽  
Air Quality Modeling ◽  
Air Quality Model ◽  
Quality Model ◽  
Desktop Computer ◽  
Quality Modeling ◽  
Reduced Complexity

<p>Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM<sub>2.5</sub>). Designing policies to reduce deaths relies on air quality modeling for estimating changes in PM<sub>2.5</sub> concentrations from many policy scenarios at high spatial resolution. However, air quality modeling typically has high requirements for computation and expertise, which limits policy design, especially in countries where most PM<sub>2.5</sub>-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM<sub>2.5</sub> concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with 4 km horizontal grid cell widths in cities. We evaluate Global InMAP performance both against measurements and a state-of-the-science chemical transport model, GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%. Global InMAP can be run on a desktop computer; simulations here took 2.6–4.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate air pollution policy assessment worldwide, providing a tool for reducing the deaths where they occur most.</p>

scholarly journals Global, High-Resolution, Reduced-Complexity Air Quality Modeling Using InMAP(Intervention Model for Air Pollution)

2021 ◽  
Author(s):  
Sumil Thakrar ◽  
Christopher Tessum ◽  
Joshua Apte ◽  
Srinidhi Balasubramanian ◽  
Dylan B Millet ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
The United States ◽  
Air Quality Modeling ◽  
Air Quality Model ◽  
Quality Model ◽  
Desktop Computer ◽  
Quality Modeling ◽  
Reduced Complexity

<p>Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM<sub>2.5</sub>). Designing policies to reduce deaths relies on air quality modeling for estimating changes in PM<sub>2.5</sub> concentrations from many policy scenarios at high spatial resolution. However, air quality modeling typically has high requirements for computation and expertise, which limits policy design, especially in countries where most PM<sub>2.5</sub>-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM<sub>2.5</sub> concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with 4 km horizontal grid cell widths in cities. We evaluate Global InMAP performance both against measurements and a state-of-the-science chemical transport model, GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%. Global InMAP can be run on a desktop computer; simulations here took 2.6–4.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate air pollution policy assessment worldwide, providing a tool for reducing the deaths where they occur most.</p>

scholarly journals Exploring the nature of air quality over southwestern Ontario: main findings from the Border Air Quality and Meteorology Study

2013 ◽  
Vol 13 (20) ◽  
pp. 10461-10482 ◽  
Author(s):  
J. R. Brook ◽  
P. A. Makar ◽  
D. M. L. Sills ◽  
K. L. Hayden ◽  
R. McLaren
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Great Lakes ◽  
The United States ◽  
Urban Heat Islands ◽  
Anthropogenic Emissions ◽  
Lake Breeze ◽  
Aerosol Formation ◽  
Air Quality Model ◽  
Quality Model

Abstract. This paper serves as an overview and discusses the main findings from the Border Air Quality and Meteorology Study (BAQS-Met) in southwestern Ontario in 2007. This region is dominated by the Great Lakes, shares borders with the United States and consistently experiences the highest ozone (O3) and fine particulate matter concentrations in Canada. The purpose of BAQS-Met was to improve our understanding of how lake-driven meteorology impacts air quality in the region, and to improve models used for forecasting and policy scenarios. Results show that lake breeze occurrence frequencies and inland penetration distances were significantly greater than realized in the past. Due to their effect on local meteorology, the lakes were found to enhance secondary O3 and aerosol formation such that local anthropogenic emissions have their impact closer to the populated source areas than would otherwise occur in the absence of the lakes. Substantial spatial heterogeneity in O3 was observed with local peaks typically 30 ppb above the regional values. Sulfate and secondary organic aerosol (SOA) enhancements were also linked to local emissions being transported in the lake breeze circulations. This study included the first detailed evaluation of regional applications of a high-resolution (2.5 km grid) air quality model in the Great Lakes region. The model showed that maxima in secondary pollutants occur in areas of convergence, in localized updrafts and in distinct pockets over the lake surfaces. These effects are caused by lake circulations interacting with the synoptic flow, with each other or with circulations induced by urban heat islands. Biogenic and anthropogenic emissions were both shown to play a role in the formation of SOA in the region. Detailed particle measurements and multivariate receptor models reveal that while individual particles are internally mixed, they often exist within more complex external mixtures. This makes it difficult to predict aerosol optical properties and further highlights the challenges facing aerosol modelling. The BAQS-Met study has led to a better understanding of the value of high-resolution (2.5 km) modelling for air quality and meteorological predictions and has led to several model improvements.

scholarly journals Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

2013 ◽  
Vol 10 (3) ◽  
pp. 1635-1645 ◽  
Author(s):  
J. O. Bash ◽  
E. J. Cooter ◽  
R. L. Dennis ◽  
J. T. Walker ◽  
J. E. Pleim
Keyword(s):  
Air Quality ◽  
The United States ◽  
United States Department ◽  
Air Quality Model ◽  
Quality Model ◽  
Coupled Modeling ◽  
Surface Exchange ◽  
Agroecosystem Model ◽  
The Us ◽  
Bidirectional Exchange

Abstract. Atmospheric ammonia (NH3) is the primary atmospheric base and an important precursor for inorganic particulate matter and when deposited NH3 contributes to surface water eutrophication, soil acidification and decline in species biodiversity. Flux measurements indicate that the air–surface exchange of NH3 is bidirectional. However, the effects of bidirectional exchange, soil biogeochemistry and human activity are not parameterized in air quality models. The US Environmental Protection Agency's (EPA) Community Multiscale Air-Quality (CMAQ) model with bidirectional NH3 exchange has been coupled with the United States Department of Agriculture's (USDA) Environmental Policy Integrated Climate (EPIC) agroecosystem model. The coupled CMAQ-EPIC model relies on EPIC fertilization timing, rate and composition while CMAQ models the soil ammonium (NH4&amp;plus;) pool by conserving the ammonium mass due to fertilization, evasion, deposition, and nitrification processes. This mechanistically coupled modeling system reduced the biases and error in NHx (NH3 &amp;plus; NH4&amp;plus;) wet deposition and in ambient aerosol concentrations in an annual 2002 Continental US (CONUS) domain simulation when compared to a 2002 annual simulation of CMAQ without bidirectional exchange. Fertilizer emissions estimated in CMAQ 5.0 with bidirectional exchange exhibits markedly different seasonal dynamics than the US EPA's National Emissions Inventory (NEI), with lower emissions in the spring and fall and higher emissions in July.

scholarly journals Global, High-Resolution, Reduced-Complexity Air Quality Modeling Using InMAP (Intervention Model for Air Pollution)

2021 ◽  
Author(s):  
Sumil Thakrar ◽  
Christopher Tessum ◽  
Joshua Apte ◽  
Srinidhi Balasubramanian ◽  
Dylan B Millet ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Transport Model ◽  
The United States ◽  
Air Quality Modeling ◽  
Air Quality Model ◽  
Desktop Computer ◽  
Policy Assessment ◽  
Quality Modeling ◽  
Reduced Complexity

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many policy scenarios at high spatial resolution. However, air quality modeling typically has high requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with 4 km horizontal grid cell widths in cities. We evaluate Global InMAP performance both against measurements and a state-of-the-science chemical transport model, GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–4.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate air pollution policy assessment worldwide, providing a screening tool for reducing the deaths where they occur most.

scholarly journals New approach for optimal electricity planning and dispatching with hourly time-scale air quality and health considerations

2015 ◽  
Vol 112 (35) ◽  
pp. 10884-10889 ◽  
Author(s):  
Paul Y. Kerl ◽  
Wenxian Zhang ◽  
Juan B. Moreno-Cruz ◽  
Athanasios Nenes ◽  
Matthew J. Realff ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plant ◽  
Power Plants ◽  
Air Pollutant ◽  
Health Impacts ◽  
Reduced Form ◽  
Air Quality Model ◽  
Quality Model ◽  
Electricity Planning

Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004–2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of $175.9 million dollars for an additional electricity generation cost of $83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies.

scholarly journals The quest for improved air quality may push China to continue its CO2reduction beyond the Paris Commitment

2020 ◽  
Vol 117 (47) ◽  
pp. 29535-29542
Author(s):  
Jia Xing ◽  
Xi Lu ◽  
Shuxiao Wang ◽  
Tong Wang ◽  
Dian Ding ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Benefit ◽  
Cost Effective ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Chinese Government ◽  
Low Carbon ◽  
Air Quality Model ◽  
Quality Model ◽  
Low Carbon Energy

China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The “Beautiful China” strategy, launched by the Chinese government in 2020, requires that all cities in China attain 35 μg/m3or below for annual mean concentration of PM2.5(particulate matter with aerodynamic diameter less than 2.5 μm) by 2035. Meanwhile, China adopts a portfolio of low-carbon policies to meet its Nationally Determined Contribution (NDC) pledged in the Paris Agreement. Previous studies demonstrated the cobenefits to air pollution reduction from implementing low-carbon energy policies. Pathways for China to achieve dual targets of both air quality and CO2mitigation, however, have not been comprehensively explored. Here, we couple an integrated assessment model and an air quality model to evaluate air quality in China through 2035 under the NDC scenario and an alternative scenario (Co-Benefit Energy [CBE]) with enhanced low-carbon policies. Results indicate that some Chinese cities cannot meet the PM2.5target under the NDC scenario by 2035, even with the strictest end-of-pipe controls. Achieving the air quality target would require further reduction in emissions of multiple air pollutants by 6 to 32%, driving additional 22% reduction in CO2emissions relative to the NDC scenario. Results show that the incremental health benefit from improved air quality of CBE exceeds 8 times the additional costs of CO2mitigation, attributed particularly to the cost-effective reduction in household PM2.5exposure. The additional low-carbon energy polices required for China’s air quality targets would lay an important foundation for its deep decarbonization aligned with the 2 °C global temperature target.

scholarly journals Review of the JCAP/JATOP Air Quality Model Study in Japan

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 943
Author(s):  
Yoshiaki Shibata ◽  
Tazuko Morikawa
Keyword(s):  
Air Quality ◽  
The United States ◽  
Atmospheric Environment ◽  
Exhaust Gas ◽  
Environmental Standards ◽  
Air Quality Model ◽  
Quality Model ◽  
Automobile Exhaust ◽  
Research Activities ◽  
Model Study

Around 1997, when JCAP (the Japan Clean Air Program) began, Japan’s atmospheric environment did not meet the environmental standards for NO2 and suspended particle matters (SPM), and strict reduction requirements for automobile exhaust gas were required. To achieve environmental standards, further cooperation between the automobile technology and fuel technology sectors was needed. In Europe and the United States, Auto-Oil programs were being implemented to reduce automobile exhaust gas, and JCAP was established as an Auto-Oil program in Japan. The Air Quality Model Study was one of the research themes and research activities continued for a total of 21 years, including JCAP I/II and JATOP I/II/III (the Japan AuTo Oil Program). JATOP was the successor program of JCAP. This paper describes the outline and main results of the JCAP/JATOP Air Quality Model Study.

scholarly journals Evaluation of a regional air-quality model with bi-directional NH<sub>3</sub> exchange coupled to an agro-ecosystem model

2012 ◽  
Vol 9 (8) ◽  
pp. 11375-11401 ◽  
Author(s):  
J. O. Bash ◽  
E. J. Cooter ◽  
R. L. Dennis ◽  
J. T. Walker ◽  
J. E. Pleim
Keyword(s):  
Air Quality ◽  
Environmental Protection Agency ◽  
Management Practices ◽  
The United States ◽  
United States Department ◽  
Air Quality Model ◽  
Quality Model ◽  
Coupled Modeling ◽  
Surface Exchange ◽  
The Us

Abstract. Atmospheric ammonia (NH3) is the primary atmospheric base and an important precursor for inorganic particulate matter and when deposited NH3 contributes to surface water eutrophication, soil acidification and decline in species biodiversity. Flux measurements indicate that the air-surface exchange of NH3 is bi-directional. However, the effects of bi-directional exchange, soil biogeochemistry and human activity are not parameterized in air quality models. The US Environmental Protection Agency (EPA)'s Community Multiscale Air-Quality (CMAQ) model with bi-directional NH3 exchange has been coupled with the United States Department of Agriculture (USDA)'s Environmental Policy Integrated Climate (EPIC) agro-ecosystem model's nitrogen geochemistry algorithms. CMAQ with bi-directional NH3 exchange coupled to EPIC connects agricultural cropping management practices to emissions and atmospheric concentrations of reduced nitrogen and models the biogeochemical feedback on NH3 air-surface exchange. This coupled modeling system reduced the biases and error in NHx (NH3 + NH4+) wet deposition and in ambient aerosol concentrations in an annual 2002 Continental US (CONUS) domain simulation when compared to a 2002 annual simulation of CMAQ without bi-directional exchange. Fertilizer emissions estimated in CMAQ 5.0 with bi-directional exchange exhibits markedly different seasonal dynamics than the US EPA's National Emissions Inventory (NEI), with lower emissions in the spring and fall and higher emissions in July.

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