Intercomparison of air quality models in a megacity: Towards an operational ensemble forecasting system for São Paulo

An intercomparison of four air quality models is performed in the tropical megacity of Sao Paulo with the perspective of developing an air quality forecasting system based on a regional model ensemble. During three contrasting periods marked by different types of pollution events, we analyze the concentrations of the main regulated pollutants (Ozone, CO, SO2, NOx, PM2.5 and PM10) compared to observations of a dense air quality monitoring network. The modeled concentrations of CO, PM and NOx are in good agreement with the observations for the temporal variability and the range of variation. However, the transport of pollutants due to biomass burning pollution events can strongly affect the air quality in the metropolitan area of Sao Paulo with increases of CO, PM2.5 and PM10, and is associated with an important inter-model variability. Our results show that each model has periods and pollutants for which it has the best agreement. The observed day-to-day variability of ozone concentration is well reproduced by the models, as well as the average diurnal cycle in terms of timing. Overall the performance for ozone of the median of the regional model ensemble is the best in terms of time and magnitude because it takes advantage of the capabilities of each model. Therefore, an ensemble prediction of regional models is promising for an operational air quality forecasting system for the megacity of Sao Paulo.This article is a direct contribution to the research themes of the Klimapolis Lab-836 oratory (klimapolis.net), which is funded by the German Federal Ministry of Education837 and Research (BMBF)

Top-down vehicle emission inventory for spatial distribution and dispersion modeling of particulate matter

Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate theuseoftheparticulatematter(PM)emissionestimationfromtheBraziliantop-downvehicleemissioninventory(VEI)of2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year−1) from vehicular sources was distributed into the urban areas of 5557municipalities,with1-km2 gridspacing,consideringtwoapproaches:(i)populationand(ii)fleetofeachcity.Acomparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling(August2015)wasperformedwithWRF-ChemforthefourmetropolitanareasofBrazilianSoutheast:BeloHorizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future

Quantifying the impact of particle matter on mortality and hospitalizations in four Brazilian metropolitan areas

Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM10 and PM2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM2.5, were 32,000±5,300 due to all-cause mortality, between 16,000±2,100 and 51,000±3,000 due non-accidental causes, between 7,300±1,300 and 16,700±1,500 due to cardiovascular disease, between 4,750±900 and 10,950±870 due ischemic heart diseases and 1,220±330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM2.5 among ‘children’ age group than for PM10 (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840±3,950 avoidable hospitalizations in the elderly related to a decrease in PM2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management

Kriging method application and traffic behavior profiles from local radar network database: A proposal to support traffic solutions and air pollution control strategies

Vehicles are commonly the main source of outdoor air pollution in urban areas and vehicular emission inventory is a tool to identify the emissions contribution from mobile sources. In this study, we developed an emission inventory to Belo Horizonte, a densely populated urban city in Brazil, with approximately 2.0 million vehicles. The vehicular emission inventory was developed applying the National Vehicle Emission Inventory model (VEIN) using emission factor from São Paulo State Environmental Protection Agency, different traffic behavior profile (constant and different diurnal cycle per vehicle type) established from local radar data and kriging interpolation method considering four different scenarios with reductions in fleet composition. The scenarios were described as according the combination between traffic behavior profiles, vehicle flow, vehicle type and a fuel consumption. The comparison between scenarios showed reductions of emissions around 8.5 % (CO), 8.8 % (CO2), when it was considered 10 % of reduction in fleet composition of passenger cars and light commercial vehicles. Considering 20 % reduction in diesel fleet composition (trucks and buses), a decrease of 8.4 % (NOx) and 8.6 % (PM) was observed. Furthermore, this work presented that the kriging method to define a spatial/temporal distributing using radar traffic data is an alternative low- cost method to investigate the effect of real traffic data on the vehicular emissions modeling. This study is pioneer in Brazil and reinforced the importance of detailing traffic activities using real data to estimate vehicular emissions in an urban area. Transportation management strategies to reduce air pollution and to assist users to reduce air pollution exposure are mandatory to create a collaborative network and build sustainable cities in the future. It is necessary more investigations methods to generate an accuracy spatial distribution aggregated coupled with different traffic behavior profile to develop actions to reduce vehicular emissions in urban areas, investigate air pollution exposure, perform project-level emissions and hot-spot analysis

Coupled models using radar network database to assess vehicular emissions in current and future scenarios

Vehicles are one of the most significant sources of air pollutant emissions in urban areas, and their real contribution always needs to be updated to predict impacts on air quality. Radar databases and traffic counts using statistical modeling is an alternative and low-cost approach to produce traffic activities data in each urban street to be used as input to predict vehicular emissions. In this work, we carried out a spatial statistical analysis of local radar data and calculated traffic flow using local radar data combined with different statistical models. Future scenarios about vehicle emission inventory to define public policies were also proposed and analyzed for Belo Horizonte (BH), a Brazilian State capital, with the third-largest metropolitan region in the country. The Normal-Neighborhood Model (i.e., the mixed effect model with random effect in the neighborhood, radar type, and in the regional area) was used to calculate traffic flow in each urban street. Results showed average reductions in CO (4.5%), NMHC (3.0%), NOx (3.0%) and PM2.5 (6.2%) emissions even with an increase in fleet composition (25% in average). The decrease is a result of the implementation of emission control programs by the government, improvements vehicles technologies, and the quality of fuels. Prediction of traffic data from radar databases has proven to be useful for avoiding the high costs of performing origin-destination surveys and traffic modeling using commercial software. Radar databases can provide many potential benefits for research and analysis in environmental and transportation planning. These findings can be incorporated in future investigations to implement public policies on vehicular emission reduction in urban areas and to advance environmental health effects research and human health risk assessment.[Display omitted]•The model Normal-Neighborhood was more suitable to perform a spatial distribution of vehicle flow.•Different fleet reduction combinations generate up to a 40% reduction in vehicle emissions.•Mobility and transportation solutions can be proposed using radar data

Traffic data in air quality modeling: a review of key variables, improvements in results, open problems and challenges in current research

Outdoor air pollution was responsible for approximately 4.2 million deaths around the world in 2016, with the emissions from road vehicles being the main source of air pollution in urban areas. To fulfill the need to identify the contribution of pollutants emitted by on-road vehicles and examine the limitations of various air quality models (boundary conditions, wind behavior representations, chemical mechanisms and reactions), a systematic review of the main traffic variables used in emissions and air quality modeling was performed. The discussion of their relationships, connections, and relevance showed a consistent sequence to generate traffic data using different traffic models. A list of key traffic variables to use as input data for vehicle emissions modeling and consequently to improve the accuracy of air quality modeling was proposed. A revision over 125 published articles was realized approaching methods to integrate traffic, emissions, air quality models, and detailing how these data can improve the results generated by the air quality model. Traffic models (macroscopic, mesoscopic, and microscopic) require variables at different levels of detail, such as traffic flow, speed, fuel consumption, and fleet composition. The emissions models (static and dynamic) are the key inputs to regional air quality models, but there is a tradeoff between the accuracy in emission estimates and the level of detail in model inputs. Meteorological data also influence the results. The conclusions showed that gaps remain on consistent emissions factors, spatial and temporal distributions, allocations of emissions on grid cells, and performance of the meteorological models. The average link-based traffic parameters are a persistent limitation. The proposed key traffic variables list point to flow per vehicle type as the most important variable. There is a need for scientific efforts to integrate traffic engineering data into emissions models to improve air quality modeling results using better traffic flow representations. Uncertainties in traffic data must first be analyzed, and accordingly a guidance with an accuracy reference for distinctive applications in different regions should be proposed

Source apportionment modelling of PM2.5 using CMAQ-ISAM over a tropical coastal-urban area

This study aims to explain the role of local emission sources to PM2.5 mass concentration in a tropical coastal-urban area, highly influenced by industrial and urban emissions, located in the Southeast of Brazil. The Integrated Source Apportionment Method (ISAM) tool was coupled with the chemistry and transport Community Multiscale Air Quality (CMAQ) model (CMAQ-ISAM) to quantify the contribution of ten emission sectors of PM2.5. The simulations were performed over five months between spring 2019 and summer 2020 using a local inventory, which was processed by the Sparse Matrix Operator Kernel Emission (SMOKE). The meteorological fields were provided by the Weather Research and Forecasting (WRF-Urban) model. The boundary and initial conditions to the CMAQ-ISAM were performed by the GEOS-Chem model. The simulations results show that the road dust resuspension (36%) and point (17%) emissions sources were the major contributors to PM2.5 mass in the Metropolitan Region of Vitória (MRV). The boundary conditions (BCON), representing the transport contribution from sources outside the domain, were also a dominant contributor in the MRV (20% on average). Furthermore, the primary atmospheric pollutants emitted by the point (14%) and shipping (7%) sectors in the MRV also affected the cities located in the south region of the domain, strengthened by the wind fields that mostly come from the northeast direction