Ambient Air Temperature, Air Quality and the Timing of Excess Mortality Among Young Men in the United States

Human Ecology ◽  
2021 ◽  
Author(s):  
Robert Bozick
Keyword(s):  
United States ◽  
Air Quality ◽  
Air Temperature ◽  
Excess Mortality ◽  
Young Men ◽  
Ambient Air ◽  
The United States ◽  
Ambient Air Temperature

scholarly journals Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

2021 ◽  
Vol 14 (6) ◽  
pp. 4617-4637
Author(s):  
Karoline K. Barkjohn ◽  
Brett Gantt ◽  
Andrea L. Clements
Keyword(s):  
United States ◽  
Air Quality ◽  
Reference Method ◽  
Ambient Air ◽  
The United States ◽  
Global Network ◽  
Raw Data ◽  
The Us ◽  
State And Local ◽  
Multiplicative Models

Abstract. PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10 000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12 000 24 h averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (US), including 16 states. Consistent with previous evaluations, under typical ambient and smoke-impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the US. A simple linear regression reduces much of this bias across most US regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 to 3 µg m−3, with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.

scholarly journals Development and Application of a United States wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

2020 ◽  
Author(s):  
Karoline K. Barkjohn ◽  
Brett Gantt ◽  
Andrea L. Clements
Keyword(s):  
United States ◽  
Air Quality ◽  
Reference Method ◽  
Ambient Air ◽  
The United States ◽  
Global Network ◽  
Raw Data ◽  
State And Local ◽  
Multiplicative Models ◽  
The U.S

Abstract. PurpleAir sensors which measure particulate matter (PM) are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10,000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12,000 24-hour averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (U.S.), including 16 states. Consistent with previous evaluations, under typical ambient and smoke impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the U.S. A simple linear regression reduces much of this bias across most U.S. regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 µg m−3 to 3 µg m−3 with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the U.S. in the AirNow Fire and Smoke Map (fire.airnow.gov) and has the potential to be successfully used in other air quality and public health applications.

scholarly journals Contiguous United States wildland fire emission estimates during 2003–2015

2018 ◽  
Author(s):  
Shawn P. Urbanski ◽  
Matt C. Reeves ◽  
Rachel Corley ◽  
Robin Silverstein ◽  
Wei Min Hao
Keyword(s):  
United States ◽  
Air Pollution ◽  
Air Quality ◽  
Emission Inventory ◽  
Ambient Air ◽  
The United States ◽  
Pollutant Emissions ◽  
Burned Area ◽  
Large Set ◽  
Fuel Loading

Abstract. Wildfires are a major source of air pollutants in the United States. Wildfire smoke can trigger severe pollution episodes with substantial impacts on public health. In addition to acute episodes, wildfires can have a marginal effect on air quality at significant distances from the source presenting significant challenges to air regulators’ efforts to meet National Ambient Air Quality Standards. Improved emission estimates are needed to quantify the contribution of wildfires to air pollution and thereby inform decision making activities related to the control and regulation of anthropogenic air pollution sources. To address the need of air regulators and land managers for improved wildfire emission estimates we developed the Missoula Fire Lab Emission Inventory (MFLEI), a retrospective, daily wildfire emission inventory for the contiguous United States (CONUS). MFLEI was produced using multiple datasets of fire activity and burned area, a newly developed wildland fuels map and an updated emission factor database. Daily burned area is based on a combination of Monitoring Trends in Burn Severity (MTBS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) burned area and active fire detection products, incident fire perimeters, and a spatial wildfire occurrence database. The fuel type classification map is a merger of a national forest type map, produced by the USDA Forest Service (USFS) Forest Inventory and Analysis (FIA) program and the Geospatial Technology and Applications Center (GTAC), with a shrub and grassland vegetation map developed by the USFS Missoula Forestry Sciences Laboratory. Forest fuel loading is from a fuel classification developed from a large set (> 26 000 sites) of FIA surface fuel measurements. Herbaceous fuel loading is estimated using site specific parameters with normalized differenced vegetation index from MODIS. Shrub fuel loading is quantified by applying numerous allometric equations linking stand structure and composition to biomass and fuels, with the structure and composition data derived from geospatial data layers of the LANDFIRE Project. MFLEI provides estimates of CONUS daily wildfire burned area, fuel consumption, and pollutant emissions at a 250 m × 250 m resolution for 2003–2015. A spatially aggregated emission product (10 km × 10 km, 1 d) with uncertainty estimates is included to provide a representation of emission uncertainties at a spatial scale pertinent to air quality modelling. MFLEI will be updated, with recent years, as the MTBS burned area product becomes available. The data associated with this article can be found at https://doi.org/10.2737/RDS-2017-0039.

scholarly journals Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments

2012 ◽  
Vol 9 (10) ◽  
pp. 4023-4035 ◽  
Author(s):  
E. J. Cooter ◽  
J. O. Bash ◽  
V. Benson ◽  
L. Ran
Keyword(s):  
United States ◽  
Air Quality ◽  
Ambient Air ◽  
The United States ◽  
Fertilizer Application ◽  
Nitrogen Fertilizers ◽  
System Level ◽  
Agricultural Crop ◽  
Modeling Framework ◽  
Continental Scale

Abstract. While nitrogen (N) is an essential element for life, human population growth and demands for energy, transportation and food can lead to excess nitrogen in the environment. A modeling framework is described and implemented to promote a more integrated, process-based and system-level approach to the estimation of ammonia (NH3) emissions which result from the application of inorganic nitrogen fertilizers to agricultural soils in the United States. The United States Department of Agriculture (USDA) Environmental Policy Integrated Climate (EPIC) model is used to simulate plant demand-driven fertilizer applications to commercial cropland throughout the continental US. This information is coupled with a process-based air quality model to produce continental-scale NH3 emission estimates. Regional cropland NH3 emissions are driven by the timing and amount of inorganic NH3 fertilizer applied, soil processes, local meteorology, and ambient air concentrations. Initial fertilizer application often occurs when crops are planted. A state-level evaluation of EPIC-simulated, cumulative planted area compares well with similar USDA reported estimates. EPIC-annual, inorganic fertilizer application amounts also agree well with reported spatial patterns produced by others, but domain-wide the EPIC values are biased about 6% low. Preliminary application of the integrated fertilizer application and air quality modeling system produces a modified geospatial pattern of seasonal NH3 emissions that improves current simulations of observed atmospheric particle nitrate concentrations. This modeling framework provides a more dynamic, flexible, and spatially and temporally resolved estimate of NH3 emissions than previous factor-based NH3 inventories, and will facilitate evaluation of alternative nitrogen and air quality policy and adaptation strategies associated with future climate and land use changes.

scholarly journals Contiguous United States wildland fire emission estimates during 2003–2015

2018 ◽  
Vol 10 (4) ◽  
pp. 2241-2274 ◽  
Author(s):  
Shawn P. Urbanski ◽  
Matt C. Reeves ◽  
Rachel E. Corley ◽  
Robin P. Silverstein ◽  
Wei Min Hao
Keyword(s):  
United States ◽  
Air Pollution ◽  
Air Quality ◽  
Emission Inventory ◽  
Ambient Air ◽  
The United States ◽  
Pollutant Emissions ◽  
Burned Area ◽  
Large Set ◽  
Fuel Loading

Abstract. Wildfires are a major source of air pollutants in the United States. Wildfire smoke can trigger severe pollution episodes with substantial impacts on public health. In addition to acute episodes, wildfires can have a marginal effect on air quality at significant distances from the source, presenting significant challenges to air regulators' efforts to meet National Ambient Air Quality Standards. Improved emission estimates are needed to quantify the contribution of wildfires to air pollution and thereby inform decision-making activities related to the control and regulation of anthropogenic air pollution sources. To address the need of air regulators and land managers for improved wildfire emission estimates, we developed the Missoula Fire Lab Emission Inventory (MFLEI), a retrospective, daily wildfire emission inventory for the contiguous United States (CONUS). MFLEI was produced using multiple datasets of fire activity and burned area, a newly developed wildland fuels map and an updated emission factor database. Daily burned area is based on a combination of Monitoring Trends in Burn Severity (MTBS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) burned area and active fire detection products, incident fire perimeters, and a spatial wildfire occurrence database. The fuel type classification map is a merger of a national forest type map, produced by the USDA Forest Service (USFS) Forest Inventory and Analysis (FIA) program and the Geospatial Technology and Applications Center (GTAC), with a shrub and grassland vegetation map developed by the USFS Missoula Forestry Sciences Laboratory. Forest fuel loading is from a fuel classification developed from a large set (> 26 000 sites) of FIA surface fuel measurements. Herbaceous fuel loading is estimated using site-specific parameters with the Normalized Difference Vegetation Index from MODIS. Shrub fuel loading is quantified by applying numerous allometric equations linking stand structure and composition to biomass and fuels, with the structure and composition data derived from geospatial data layers of the LANDFIRE project. MFLEI provides estimates of CONUS daily wildfire burned area, fuel consumption, and pollutant emissions at a 250 m × 250 m resolution for 2003–2015. A spatially aggregated emission product (10 km × 10 km, 1 day) with uncertainty estimates is included to provide a representation of emission uncertainties at a spatial scale pertinent to air quality modeling. MFLEI will be updated, with recent years, as the MTBS burned area product becomes available. The data associated with this article can be found at https://doi.org/10.2737/RDS-2017-0039 (Urbanski et al., 2017).

scholarly journals Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments

2012 ◽  
Vol 9 (5) ◽  
pp. 6095-6127 ◽  
Author(s):  
E. J. Cooter ◽  
J. O. Bash ◽  
V. Benson ◽  
L. Ran
Keyword(s):  
United States ◽  
Air Quality ◽  
Spatial Patterns ◽  
Crop Management ◽  
Ambient Air ◽  
The United States ◽  
Fertilizer Application ◽  
System Level ◽  
Agricultural Crop ◽  
Modeling Framework

Abstract. While nitrogen (N) is an essential element for life, human population growth and demands for energy, transportation and food can lead to excess nitrogen in the environment. A modeling framework is described and implemented, to promote a more integrated, process-based and system-level approach to the estimation of ammonia (NH3) emissions resulting from the application of inorganic nitrogen fertilizers to agricultural soils in the United States. The United States Department of Agriculture (USDA) Environmental Policy Integrated Climate (EPIC) model is used to simulate plant demand-driven fertilizer applications to commercial cropland throughout the continental US. This information is coupled with a process-based air quality model to produce continental-scale NH3 emission estimates. Regional cropland NH3 emissions are driven by the timing and amount of fertilizer applied, local meteorology, and ambient air concentrations. An evaluation of EPIC-simulated crop management activities associated with fertilizer application at planting compared with similar USDA state-level event estimates shows temporally progressive spatial patterns that agree well with one another. EPIC annual inorganic fertilizer application amounts also agree well with reported spatial patterns produced by others, but domain-wide the EPIC values are biased about 6 % low. Preliminary application of the integrated fertilizer application and air quality modeling system produces a modified geospatial pattern of seasonal NH3 emissions that improves current simulations of observed atmospheric nitrate concentrations. This modeling framework provides a more dynamic, flexible, and spatially and temporally resolved estimate of NH3 emissions than previous factor-based NH3 inventories, and will facilitate evaluation of alternative nitrogen and air quality policy and adaptation strategies associated with future climate and land use changes.

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