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 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 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 Air quality modeling with WRF-Chem v3.5 in East Asia: sensitivity to emissions and evaluation of simulated air quality

2016 ◽  
Vol 9 (3) ◽  
pp. 1201-1218 ◽  
Author(s):  
Min Zhong ◽  
Eri Saikawa ◽  
Yang Liu ◽  
Vaishali Naik ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Air Quality ◽  
East Asia ◽  
Model Performance ◽  
Air Quality Modeling ◽  
Performance Criteria ◽  
Air Quality Model ◽  
Quality Model ◽  
Provincial Level ◽  
Mixing Ratios ◽  
Quality Modeling

Abstract. We conducted simulations using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.5 to study air quality in East Asia at a spatial resolution of 20 km  ×  20 km. We find large discrepancies between two existing emissions inventories: the Regional Emission Inventory in ASia version 2 (REAS) and the Emissions Database for Global Atmospheric Research version 4.2 (EDGAR) at the provincial level in China, with maximum differences of up to 500 % for CO emissions, 190 % for NO, and 160 % for primary PM10. Such discrepancies in the magnitude and the spatial distribution of emissions for various species lead to a 40–70 % difference in surface PM10 concentrations, 16–20 % in surface O3 mixing ratios, and over 100 % in SO2 and NO2 mixing ratios in the polluted areas of China. WRF-Chem is sensitive to emissions, with the REAS-based simulation reproducing observed concentrations and mixing ratios better than the EDGAR-based simulation for July 2007. We conduct additional model simulations using REAS emissions for January, April, July, and October of 2007 and evaluate simulations with available ground-level observations. The model results illustrate clear regional variations in the seasonal cycle of surface PM10 and O3 over East Asia. The model meets the air quality model performance criteria for both PM10 (mean fractional bias, MFB ⩽ ±60 %) and O3 (MFB ⩽ ±15 %) at most of the observation sites, although the model underestimates PM10 over northeastern China in January. The model predicts the observed SO2 well at sites in Japan, while it tends to overestimate SO2 in China in July and October. The model underestimates observed NO2 in all 4 months. Our study highlights the importance of constraining emissions at the provincial level for regional air quality modeling over East Asia. Our results suggest that future work should focus on the improvement of provincial-level emissions especially estimating primary PM, SO2, and NOx.

Chemical Mechanisms for Air Quality Modeling and Their Applications

2015 ◽  
Author(s):  
Jack G. Calvert ◽  
John J. Orlando ◽  
William R. Stockwell ◽  
Timothy J. Wallington
Keyword(s):  
Air Quality ◽  
Organic Compounds ◽  
Air Quality Modeling ◽  
Chemical Mechanism ◽  
Air Quality Model ◽  
Quality Model ◽  
Chemical Mechanisms ◽  
Number Of Species ◽  
Quality Modeling ◽  
Aggregation Scheme

A chemical mechanism is a critical component of an air quality model. Tropospheric gas phase chemical mechanisms for air quality modeling are designed to simulate the production of ozone, acids, and aerosol precursors. Therefore, their focus is on the oxidation chemistry of ozone, nitrogen oxides, sulfur compounds, and organic compounds. Figure IX-A-1 is an overview of the most important cycles of radicals that must be represented in a chemical mechanism for air quality modeling. The processes shown schematically on one level may appear to be relatively simple, but, in reality, the chemical mechanism is extremely complicated due to the very large number of organic compounds present in the atmosphere. Atmospheric chemistry mechanisms are based on laboratory data and tested against environmental experiments and field measurements (Stockwell et al., 2012). Usually, the mechanism is considered to consist of chemical species and their reactions and rate coefficients, along with the photochemical data (used to calculate photolysis frequencies). An atmospheric chemical mechanism employed in an air quality model could be considered to include the rules for aggregating emissions and initial concentrations into species (Middleton et al., 1990). There are many thousands of volatile organic compounds (VOCs) emitted into the atmosphere, and each has its own decomposition mechanism that determines the effect of the VOC on ozone production. It is critical for a chemical mechanism to characterize the chemistry of the VOCs and their differences in chemical reactivity as accurately as possible. Middleton et al. (1990) pointed out that air quality models have only a limited number of species compared to emission inventories. An emissions aggregation scheme is the process of mapping a detailed emissions inventory into the limited number of species used in an air quality model. The scheme is an important component of any model chemical mechanism. Middleton et al. published their aggregation process for the mechanism used in the Regional Acid Deposition Model (RADM2, Stockwell et al., 1990), but, too often, the emissions aggregation scheme for a given chemical mechanism is in the gray literature and difficult to access.

Evaluating NOx emission inventories for regulatory air quality modeling using satellite and air quality model data

2015 ◽  
Vol 117 ◽  
pp. 1-8 ◽  
Author(s):  
Susan Kemball-Cook ◽  
Greg Yarwood ◽  
Jeremiah Johnson ◽  
Bright Dornblaser ◽  
Mark Estes
Keyword(s):  
Air Quality ◽  
Air Quality Modeling ◽  
Nox Emission ◽  
Emission Inventories ◽  
Model Data ◽  
Air Quality Model ◽  
Quality Model ◽  
Quality Modeling

scholarly journals Emission Inventories and Particulate Matter Air Quality Modeling over the Pearl River Delta Region

2021 ◽  
Vol 18 (8) ◽  
pp. 4155
Author(s):  
Diogo Lopes ◽  
Joana Ferreira ◽  
Ka In Hoi ◽  
Ka-Veng Yuen ◽  
Kai Meng Mok ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pearl River Delta ◽  
Air Quality Modeling ◽  
Pearl River ◽  
Emission Inventories ◽  
Atmospheric Emission ◽  
The Pearl River Delta ◽  
Quality Modeling ◽  
The Pearl River

The Pearl River Delta (PRD) region is located on the southeast coast of mainland China and it is an important economic hub. The high levels of particulate matter (PM) in the atmosphere, however, and poor visibility have become a complex environmental problem for the region. Air quality modeling systems are useful to understand the temporal and spatial distribution of air pollution, making use of atmospheric emission data as inputs. Over the years, several atmospheric emission inventories have been developed for the Asia region. The main purpose of this work is to evaluate the performance of the air quality modeling system for simulating PM concentrations over the PRD using three atmospheric emission inventories (i.e., EDGAR, REAS and MIX) during a winter and a summer period. In general, there is a tendency to underestimate PM levels, but results based on the EDGAR emission inventory show slightly better accuracy. However, improvements in the spatial and temporal disaggregation of emissions are still needed to properly represent PRD air quality. This study’s comparison of the three emission inventories’ data, as well as their PM simulating outcomes, generates recommendations for future improvements to atmospheric emission inventories and our understanding of air pollution problems in the PRD region.

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.

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