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. 2020 Aug 1:234:117543-11753.
doi: 10.1016/j.atmosenv.2020.117543.

Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands

K R Baker  1 , T K V Nguyen  2 , N Sareen  3 , B H Henderson  1
Affiliations

Meteorological and Air Quality Modeling for Hawaii, Puerto Rico, and Virgin Islands

K R Baker et al. Atmos Environ (1994). .

Abstract

A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.

Keywords: CMAQ; Hawaii; O3; PM2.5; Puerto Rico; Virgin Islands; WRF.

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Figures

Figure 1.
Figure 1.
The spatial extent of the 27, 9, and 3 km model domains for Hawaii and Puerto Rico/Virgin Islands. Terrain shown at 3 km resolution for Hawaii and 27 km resolution for Puerto Rico and surrounding areas.
Figure 2.
Figure 2.
Annual average model predicted speciated PM2.5 components, SO2, NO2, and O3 for the Hawaiian Islands. Oahu is shown in the lower left insert. Measurements are shown with circles. More information about the monitors and locations are provided in Table S1.
Figure 3.
Figure 3.
Annual average model predicted speciated PM2.5 components, SO2, NO2, and O3 for Puerto Rico. Measurements are shown with circles. More information about the monitors and locations are provided in Table S1.
Figure 4.
Figure 4.
Daily average speciated PM2.5 components shown for the IMPROVE and CSN monitors in Hawaii and Puerto Rico/Virgin Islands. Each row represents information for a specific monitor. More information about the monitors and locations are provided in Table S1 and Figures S1–S2.
Figure 5.
Figure 5.
Distribution of hourly measurements at monitors in Hawaii. More information about the monitors and locations are provided in Table S1 and Figure S1.
Figure 6.
Figure 6.
Distribution of hourly measurements at monitors in Puerto Rico. More information about the monitors and locations are provided in Table S1 and Figure S2.
Figure 7.
Figure 7.
Daily PM2.5 sodium plus chloride and daily PM coarse model predictions and measurements at IMPROVE and CSN monitors in Hawaii and Puerto Rico/Virgin Islands. More information about the monitors and locations are provided in Table S1 and Figures S1–S2.
Figure 8.
Figure 8.
Model predicted PM2.5 organic aerosol January through March average wildland fire impacts (color) and HMS fire detections (crosses) for a) Maui, b) Hawaii, and c) Oahu. The period average difference in PM2.5 organic aerosol between wildfire predictions using FINN (higher impacts shown with cool colors) and SmartFire2/BlueSky (higher impacts shown with hot colors) are also shown for the island chain (panel f). HMS detections are d) totaled by month and e) hour of the day for Oahu, Maui, and Hawaii.
Figure 9.
Figure 9.
Vertical measurements of O3 from the Hilo ozonesonde (STN109, left), paired model predictions (center), and ratio (right). Measurements and predictions are moving averages of 4 ozonesonde releases (~1 month) over 2016 and measurements were averaged within the model vertical layer structure.
Figure 10.
Figure 10.
Bias for temperature, water vapor mixing ratio (WVMR), and wind speed is shown over all monitors in the 3 km Hawaii modeling domain is shown aggregated by hour of the day and month. The difference in wind vector (wind displacement) is similarly aggregated. Higher density values (and warmer colors) represent a higher percentage of values that would be clustered in a regular scatter plot.
Figure 11.
Figure 11.
Model predicted and measured vertical profiles of wind direction, wind speed, relative humidity, and temperature at Lihue Airport for the entire year of 2016 at 3 km resolution.
See this image and copyright information in PMC

References

    1. Baker K, Woody M, Valin L, Szykman J, Yates E, Iraci L, Choi H, Soja A, Koplitz S, Zhou L, 2018. Photochemical model evaluation of 2013 California wild fire air quality impacts using surface, aircraft, and satellite data. Science of The Total Environment 637, 1137–1149. - PubMed
    1. Baker KR, Emery C, Dolwick P, Yarwood G, 2015. Photochemical grid model estimates of lateral boundary contributions to ozone and particulate matter across the continental United States. Atmospheric Environment 123, 49–62.
    1. Baker KR, Kelly JT, 2014. Single source impacts estimated with photochemical model source sensitivity and apportionment approaches. Atmospheric Environment 96, 266–274.
    1. Baker KR, Kotchenruther RA, Hudman RC, 2016. Estimating ozone and secondary PM 2.5 impacts from hypothetical single source emissions in the central and eastern United States. Atmospheric Pollution Research 7, 122–133.
    1. Baker KR, Woody MC, 2017. Assessing Model Characterization of Single Source Secondary Pollutant Impacts Using 2013 SENEX Field Study Measurements. Environmental Science & Technology 51, 3833–3842. - PMC - PubMed

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