Operational Air Quality Forecast Research Articles

Enhancing real-time PM2.5 forecasts: A hybrid approach of WRF-CMAQ model and CNN algorithm

As fine particulate matter (PM2.5) poses significant environmental and human health risks, there is an urgent need for accurate forecasting systems. In Taiwan, the current air quality forecasting (AQF) system based on the Weather Research and Forecasting meteorological model and Community Multiscale Air Quality model provides essential predictions but is limited by biases and computational complexities. This study introduces a convolutional neural network (CNN)-based PM2.5 forecasting model to enhance prediction accuracy. The CNN model incorporates hourly PM2.5 concentrations from surface observations and the AQF system, along with synoptic weather patterns (SWPs), to predict PM2.5 levels up to 72 h in advance. Three CNN models were developed: CNN-BASE (without SWPs), CNN-SWP (with SWPs), and CNN-SWPW (with SWPs and a weighted loss function). Performance assessment reveals a significant reduction in the mean RMSE of 72-h PM2.5 prediction, from 10.48 μg/m3 with the AQF system to 6.88 μg/m3 with the CNN-BASE model. However, CNN-BASE showed the lowest prediction accuracy for high PM2.5 concentrations (only 26.2%) due to a small subset of samples. Including SWPs improves the model's ability to capture meteorological influences, enhancing predictions of high PM2.5 concentrations. Furthermore, CNN-SWPW incorporates a weighted loss function to address imbalanced sample size distributions, further enhancing the accuracy of high PM2.5 predictions. This study demonstrates the potential of CNNs in operational air quality forecasting.

Reducing Resolution Dependency of Dust Emission Modeling Using Albedo‐Based Wind Friction

AbstractNumerical simulations of dust emission processes are essential for dust cycle modeling and dust‐atmosphere interactions. Models have coarse spatial resolutions which, without tackling sub‐grid scale heterogeneity, bias finely resolved dust emission. Soil surface wind friction velocity (us*) drives dust emission non‐linearly with increasing model resolution, due mainly to thresholds of sediment entrainment. Albedo is area‐integrated, scales linearly with resolution, is related to us* and hence represents its sub‐grid scale heterogeneity. Calibrated albedo‐based global dust emission estimates decreased by only 2 Tg y−1 (10.5%) upscaled from 0.5 to 111 km, largely independent of resolution. Without adjusting wind fields, this scaling uncertainty is within recent estimates of global dust emission model uncertainty (±14.9 Tg y−1). This intrinsic scaling capability of the albedo‐based approach offers considerable potential to reduce resolution dependency of dust cycle modeling and improve the representation of local dust emission in Earth system models and operational air quality forecasting.

Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

AbstractAn intercomparison of four regional air quality models is performed in the tropical megacity of São Paulo with the perspective of developing a forecasting system based on a model ensemble. Modeled concentrations of the main regulated pollutants are compared with combined observations in the megacity center, after analyzing the spatial scale of representativeness of air monitoring stations. During three contrasting periods characterized by different types of pollution events, the hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10) modeled by the ensemble are in moderate agreement with observations. The median of the ensemble provides the best performance (R ≈ 0.7 for CO, 0.7 for NOx, 0.5 for SO2, 0.5 for PM2.5, and 0.4 for PM10) because each model has periods and pollutants for which it has the best agreement. NOx concentration is modeled with a large inter‐model variability, highlighting potential for improvement of anthropogenic emissions. Pollutants transported by biomass burning events strongly affect the air quality in São Paulo and are associated with significant inter‐model variability. Modeled hourly concentration of ozone (O3) is overestimated during the day (≈20 ppb) and underestimated at night (≈10 ppb), while nitrogen dioxide (NO2) is overestimated at night (≈20 ppb). The observed O3 concentration is best reproduced by the median of the ensemble (R ≈ 0.8), taking advantage of the variable performance of the models. Therefore, an operational air quality forecast system based on a regional model ensemble is promising for São Paulo.

Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

Performance of high resolution (400\xa0m) PM2.5 forecast over Delhi

This study reports a very high-resolution (400 m grid-spacing) operational air quality forecasting system developed to alert residents of Delhi and the National Capital Region (NCR) about forthcoming acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real-time aerosol observations from in situ and space-borne platform in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to produce a 72-h forecast daily in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM2.5 observations improves the initial condition for surface PM2.5 by about 45 µg/m3 (about 50%).The accuracy of the forecast degrades slightly with lead time as mean bias increase from + 2.5 µg/m3 on the first day to − 17 µg/m3 on the third day of forecast. Our forecast is found to be very skillful both for PM2.5 concentration and unhealthy/ very unhealthy air quality index categories, and has been helping the decision-makers in Delhi make informed decisions.

Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO2) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO2 emissions due to the lockdown were seen, with an average 40% (roughly 10 kt[NO2]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO2]/yr to 40 kt[NO2]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO2 columns.

Development of the User Requirements for the Canadian WildFireSat Satellite Mission

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).

Statistical Forecast Applied to Two Macao Air Monitoring Stations

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on multiple regression (MR) analysis were developed successfully for Macao to predict the next day concentrations of PM10, PM2.5, and NO2. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.89 to 0.92) for all pollutants. The models utilized meteorological and air quality variables based on five years of historical data, from 2013 to 2017. The data from 2013 to 2016 were used to develop the statistical models and data from 2017 were used for validation purposes. A wide range of meteorological and air quality variables were identified, and only some were selected as significant dependent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-hour levels. The models were applied in forecasting the next day average daily concentrations for PM10, PM2.5, and NO2 for the air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

Macao air quality forecast using statistical methods

The levels of air pollution in Macao often exceeded the levels recommended by WHO. In order for the population to take precautionary measures and avoid further health risks under high pollutant exposure, it is important to develop a reliable air quality forecast. Statistical models based on linear multiple regression (MR) and classification and regression trees (CART) analysis were developed successfully, for Macao, to predict the next day concentrations of NO2, PM10, PM2.5, and O3. All the developed models were statistically significantly valid with a 95% confidence level with high coefficients of determination (from 0.78 to 0.93) for all pollutants. The models utilized meteorological and air quality variables based on 5 years of historical data, from 2013 to 2017. Data from 2013 to 2016 were used to develop the statistical models and data from 2017 was used for validation purposes. A wide range of meteorological and air quality variables was identified, and only some were selected as significant independent variables. Meteorological variables were selected from an extensive list of variables, including geopotential height, relative humidity, atmospheric stability, and air temperature at different vertical levels. Air quality variables translate the resilience of the recent past concentrations of each pollutant and usually are maximum and/or the average of latest 24-h levels. The models were applied in forecasting the next day average daily concentrations for NO2 and PM and maximum hourly O3 levels for five air quality monitoring stations. The results are expected to be an operational air quality forecast for Macao.

CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain)

Abstract. The NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona.

Assessing the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions: current and future modelled scenarios

Abstract. A first regional assessment of the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions was conducted in this study. Model simulations were carried out on a limited-area domain (at 15 km horizontal resolution) centred over the Canadian Arctic, using the Environment and Climate Change Canada's on-line air quality forecast model, GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and CHemistry), to investigate the contribution from the marine shipping emissions over the Canadian Arctic waters (at both present and projected future levels) to ambient concentrations of criteria pollutants (O3, PM2.5, NO2, and SO2), atmospheric deposition of sulfur (S) and nitrogen (N), and atmospheric loading and deposition of black carbon (BC) in the Arctic. Several model upgrades were introduced for this study, including the treatment of sea ice in the dry deposition parameterization, chemical lateral boundary conditions, and the inclusion of North American wildfire emissions. The model is shown to have similar skills in predicting ambient O3 and PM2.5 concentrations in the Canadian Arctic and northern regions, as the current operational air quality forecast models in North America and Europe. In particular, the model is able to simulate the observed O3 and PM components well at the Canadian high Arctic site, Alert. The model assessment shows that, at the current (2010) level, Arctic shipping emissions contribute to less than 1 % of ambient O3 concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO2 and SO2 concentrations, at 10 %–50 % and 20 %–100 %, respectively. At the projected 2030 business-as-usual (BAU) level, the impact of Arctic shipping emissions is predicted to increase to up to 5 % in ambient O3 concentration over a broad region of the Canadian Arctic and to 5 %–20 % in ambient PM2.5 concentration over the shipping channels. In contrast, if emission controls such as the ones implemented in the current North American Emission Control Area (NA ECA) are to be put in place over the Canadian Arctic waters, the impact of shipping to ambient criteria pollutants would be significantly reduced. For example, with NA-ECA-like controls, the shipping contributions to the population-weighted concentrations of SO2 and PM2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulfur and nitrogen is small at the current level, < 5 %, but is expected to increase to up to 20 % for sulfur and 50 % for nitrogen under the 2030 BAU scenario. At the current level, Canadian Arctic shipping also makes only small contributions to BC column loading and BC deposition, with < 0.1 % on average and up to 2 % locally over the eastern Canadian Arctic for the former, and between 0.1 % and 0.5 % over the shipping channels for the latter. The impacts are again predicted to increase at the projected 2030 BAU level, particularly over the Baffin Island and Baffin Bay area in response to the projected increase in ship traffic there, e.g., up to 15 % on BC column loading and locally exceeding 30 % on BC deposition. Overall, the study indicates that shipping-induced changes in atmospheric composition and deposition are at regional to local scales (particularly in the Arctic). Climate feedbacks are thus likely to act at these scales, so climate impact assessments will require modelling undertaken at much finer resolutions than those used in the existing radiative forcing and climate impact assessments.

Impact of time-dependent chemical boundary conditions on tropospheric ozone simulation with WRF-Chem: An experiment over the Metropolitan Area of São Paulo

WRF-Chem (Weather Research and Forecasting with Chemistry Model) is being used to perform air quality forecast over the southeast Brazil with a domain centered in the Metropolitan Area of São Paulo (MASP). The simulations are used to understand the pollutants behavior concerning the fluxes to and from the urban areas, to examine the formation of secondary pollutants (e.g., tropospheric ozone), and to validate the efficacy of air quality policies implementation, particularly the ones that are related to the vehicular fuel used in the region. One important factor to improve the air quality simulations is the configuration of suited inflow boundary conditions for the chemical species. In that context, the objective of this study is to assess the impacts of using time-dependent Chemical Lateral Boundary Conditions (CBC) on tropospheric ozone simulations with WRF-Chem. Two simulations were carried out using Carbon-Bond Mechanism version Z (CBMZ). The first simulation was a set-up to run with WRF-Chem default CBC, and the second one using time-dependent CBC obtained from MOZART-4 (Model for Ozone and Related chemical Tracers, version 4) simulations. The period of study was from May 15 to May 18 and from October 30 to November 1, 2006, when a comprehensive experiment with ozonesondes launching and surface measurements was performed. Results show that using MOZART-4 CBC reduced mean bias, RMSE and slightly improved correlation coefficients for ground level ozone as well as ozone vertical profile above 3 km. These improvements were more significant during periods with lower photochemical activity. However, underestimation of ozone concentration peaks was also observed in this period, which can be an issue for operational air quality forecasting.

Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016

The Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to develop an operational air quality forecast system for the Metropolitan Area of Lima-Callao (MALC), Peru, that is affected by high particulate matter concentrations episodes. In this work, we describe the implementation of an operational air quality-forecasting platform to be used in the elaboration of public policies by decision makers, and as a research tool to evaluate the formation and transport of air pollutants in the MALC. To examine the skills of this new system, an air pollution event in April 2016 exhibiting unusually elevated PM2.5 concentrations was simulated and compared against in situ air quality measurements. In addition, a Model Output Statistic (MOS) algorithm has been developed to improve outputs of inhalable particulate matter (PM10) and fine particulate matter (PM2.5) from the WRF-Chem model. The obtained results showed that MOS increased the accuracy in terms of mean normalized bias for PM10 and PM2.5 from -43.1% and 71.3% to 3.1%, 7.3%, respectively. In addition, the mean normalized gross error for PM10 and PM2.5 were reduced from 48% and 92.3% to 13.4% and 10.1%, respectively. The WRF-Chem Model results showed an appropriate relationship between of temperature and relative humidity with observations during April 2016. Mean normalized bias for temperature and relative humidity were approximately - 0.6% and 1.1% respectively. In addition, the mean normalized gross error for temperature and relative humidity were approximately 4.0% and 0.1% respectively. The results showed that this modelling system can be a useful tool for the analysis of air quality in MALC.

Multi-Year (2013–2016) PM2.5 Wildfire Pollution Exposure over North America as Determined from Operational Air Quality Forecasts

FireWork is an on-line, one-way coupled meteorology–chemistry model based on near-real-time wildfire emissions. It was developed by Environment and Climate Change Canada to deliver operational real-time forecasts of biomass-burning pollutants, in particular fine particulate matter (PM2.5), over North America. Such forecasts provide guidance for early air quality alerts that could reduce air pollution exposure and protect human health. A multi-year (2013–2016) analysis of FireWork forecasts over a five-month period (May to September) was conducted. This work used an archive of FireWork outputs to quantify wildfire contributions to total PM2.5 surface concentrations across North America. Different concentration thresholds (0.2 to 28 µg/m3) and averaging periods (24 h to five months) were considered. Analysis suggested that, on average over the fire season, 76% of Canadians and 69% of Americans were affected by seasonal wildfire-related PM2.5 concentrations above 0.2 µg/m3. These effects were particularly pronounced in July and August. Futhermore, the analysis showed that fire emissions contributed more than 1 µg/m3 of daily average PM2.5 concentrations on more than 30% of days in the western USA and northwestern Canada during the fire season.

The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season

ABSTRACTEnvironment and Climate Change Canada’s FireWork air quality (AQ) forecast system for North America with near-real-time biomass burning emissions has been running experimentally during the Canadian wildfire season since 2013. The system runs twice per day with model initializations at 00 UTC and 12 UTC, and produces numerical AQ forecast guidance with 48-hr lead time. In this work we describe the FireWork system, which incorporates near-real-time biomass burning emissions based on the Canadian Wildland Fire Information System (CWFIS) as an input to the operational Regional Air Quality Deterministic Prediction System (RAQDPS). To demonstrate the capability of the system we analyzed two forecast periods in 2015 (June 2–July 15, and August 15–31) when fire activity was high, and observed fire-smoke-impacted areas in western Canada and the western United States. Modeled PM2.5 surface concentrations were compared with surface measurements and benchmarked with results from the operational RAQDPS, which did not consider near-real-time biomass burning emissions. Model performance statistics showed that FireWork outperformed RAQDPS with improvements in forecast hourly PM2.5 across the region; the results were especially significant for stations near the path of fire plume trajectories. Although the hourly PM2.5 concentrations predicted by FireWork still displayed bias for areas with active fires for these two periods (mean bias [MB] of –7.3 µg m−3 and 3.1 µg m−3), it showed better forecast skill than the RAQDPS (MB of –11.7 µg m−3 and –5.8 µg m−3) and demonstrated a greater ability to capture temporal variability of episodic PM2.5 events (correlation coefficient values of 0.50 and 0.69 for FireWork compared to 0.03 and 0.11 for RAQDPS). A categorical forecast comparison based on an hourly PM2.5 threshold of 30 µg m−3 also showed improved scores for probability of detection (POD), critical success index (CSI), and false alarm rate (FAR).Implications: Smoke from wildfires can have a large impact on regional air quality (AQ) and can expose populations to elevated pollution levels. Environment and Climate Change Canada has been producing operational air quality forecasts for all of Canada since 2009 and is now working to include near-real-time wildfire emissions (NRTWE) in its operational AQ forecasting system. An experimental forecast system named FireWork, which includes NRTWE, has been undergoing testing and evaluation since 2013. A performance analysis of FireWork forecasts for the 2015 wildfire season shows that FireWork provides significant improvements to surface PM2.5 forecasts and valuable guidance to regional forecasters and first responders.

Development and implementation of a remote-sensing and in situ data-assimilating version of CMAQ for operational PM2.5 forecasting. Part 1: MODIS aerosol optical depth (AOD) data-assimilation design and testing

This two-part paper reports on the development, implementation, and improvement of a version of the Community Multi-Scale Air Quality (CMAQ) model that assimilates real-time remotely-sensed aerosol optical depth (AOD) information and ground-based PM2.5 monitor data in routine prognostic application. The model is being used by operational air quality forecasters to help guide their daily issuance of state or local-agency-based air quality alerts (e.g. action days, health advisories). Part 1 describes the development and testing of the initial assimilation capability, which was implemented offline in partnership with NASA and the Visibility Improvement State and Tribal Association of the Southeast (VISTAS) Regional Planning Organization (RPO). In the initial effort, MODIS-derived aerosol optical depth (AOD) data are input into a variational data-assimilation scheme using both the traditional Dark Target and relatively new “Deep Blue” retrieval methods. Evaluation of the developmental offline version, reported in Part 1 here, showed sufficient promise to implement the capability within the online, prognostic operational model described in Part 2. In Part 2, the addition of real-time surface PM2.5 monitoring data to improve the assimilation and an initial evaluation of the prognostic modeling system across the continental United States (CONUS) is presented.Implications: Air quality forecasts are now routinely used to understand when air pollution may reach unhealthy levels. For the first time, an operational air quality forecast model that includes the assimilation of remotely-sensed aerosol optical depth and ground based PM2.5 observations is being used. The assimilation enables quantifiable improvements in model forecast skill, which improves confidence in the accuracy of the officially-issued forecasts. This helps air quality stakeholders be more effective in taking mitigating actions (reducing power consumption, ride-sharing, etc.) and avoiding exposures that could otherwise result in more serious air quality episodes or more deleterious health effects.

PRESAXIO regional air quality modelling system: validation and applications

Different operational air quality forecasts (ESSEM Cost Action 0602) are currently available in Europe. However, most of them are not suitable for regional application and, also, their coarse outputs do not fit well with air quality management needs. In this work, PRESAXIO regional air quality modelling system is validated and applied over Galicia (NW of Spain), including WRF and CHIMERE models. WRF validation is done by comparison with surface meteorological measurements; CHIMERE validation applies DELTA tool with AirBase data. PRESAXIO applications include an operational air quality forecast, design of a regional air quality monitoring network, and impact assessment of EMEP emissions changes in regional O 3 peaks.

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) – direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km 2, and 93% of all reference fires > 10 km 2. The burned area was highly correlated ( R 2 = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km 2 in size, improving to ± 31% at a fire size of 100 km 2. Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km 2, and 66% results from fires > 100 km 2). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km 2 spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.

Adaptation of the BOXURB model in a southeastern European environment: the case of Athens

The present work describes the adaptation and application of an operational air quality forecast model in a net grid of (1 x 1) km(2) emission source resolution, covering the Greater Area of Athens, Greece. The model is used to estimate the hourly concentrations of nitrogen oxides at a receptor grid of 17 monitoring stations for the 10-year period 1995-2004. The analysis reveals that the best model performance occurs under heavy-traffic conditions, whereas in rural and industrial areas, the model performance is worse.

The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

A sensitivity study is performed to examine the impact of lateral boundary conditions (LBCs) on the NOAA-EPA operational Air Quality Forecast Guidance over continental USA. We examined six LBCS: the fixed profile LBC, three global LBCs, and two ozonesonde LBCs for summer 2006. The simulated results from these six runs are compared to IONS ozonesonde and surface ozone measurements from August 1 to 5, 2006. The choice of LBCs can affect the ozone prediction throughout the domain, and mainly influence the predictions in upper altitude or near inflow boundaries, such as the US west coast and the northern border. Statistical results shows that the use of global model predictions for LBCs could improve the correlation coefficients of surface ozone prediction over the US west coast, but could also increase the ozone mean bias in most regions of the domain depending on global models. In this study, the use of the MOZART (Model for Ozone And Related chemical Tracers) prediction for CMAQ (Community Multiscale Air Quality) LBC shows a better surface ozone prediction than that with fixed LBC, especially over the US west coast. The LBCs derived from ozonesonde measurements yielded better O3 correlations in the upper troposphere.