Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

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

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.

Surface And Aloft NO2 Pollution Over The Greater Tokyo Area Observed By Ground-Based And MAX-DOAS Measurements Bridged By Regional Air Quality Modeling

To advance our understanding of surface and aloft nitrogen dioxide (NO2) pollution, this study extensively evaluated NO2 concentrations simulated by the regional air quality modeling system with a horizontal grid resolution of 1.3 km by using the Atmospheric Environmental Regional Observation System (AEROS) ground-based observation network and aloft measurement by multi-axis differential optical absorption spectroscopy (MAX-DOAS) over the greater Tokyo area. Observations are usually limited to the surface level, and gaps remain in our understanding of the behavior of air pollutants above the near-surface layer, particularly within the planetary boundary layer (PBL). Therefore, MAX-DOAS measurement was used, which observes scattered sunlight in the ultraviolet/visible range at several elevation angles between the horizon and zenith to determine the aloft NO2 pollution averaged over 0-1 km. In total, four MAX-DOAS measurement systems at Chiba University (35.63°N, 140.10°E) systematically covered the north, east, west, and south directions to capture the aloft NO2 pollution over the greater Tokyo area. The target period was Chiba-Campaign 2015 conducted from 9 to 23 November 2015. The evaluations showed that the air quality modeling system can generally capture the observed behavior of both surface and aloft NO2 pollution in terms of spatial and temporal coverage. The diurnal variation, which typically showed an increase from evening to early morning without daylight and a decrease during the daytime, was also captured by the model. During Chiba-Campaign 2015, two cases of episodic higher NO2 concentration were identified: one during the nighttime and the other during the daytime as different diurnal patterns. These were related to a stagnant wind field, with the latter also connected to a lower PBL height in cloudy conditions. Comparison of the modeled surface and aloft NO2 concentrations showed that aloft NO2 concentration exhibited a strong linear correlation with surface NO2 concentration, with the aloft value scaled to 0.4-0.5-fold the surface value, irrespective of whether the day was clean or polluted. This scaling value was lower during the nighttime and higher during the daytime. Based on this synergetic analysis of surface and aloft observation bridged by modeling simulation, this study contributes to fostering understanding of aloft NO2 pollution.

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

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.

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

<p>Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM_2.5). Designing policies to reduce deaths relies on air quality modeling for estimating changes in PM_2.5 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 PM_2.5-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 PM_2.5 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%. 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>