RESUMO

Air pollutants caused by traffic has become a topic of global interest due to its impact on human health and the environment, making high-resolution emission inventories effective mechanisms for air quality management. This study proposes the development of a high-resolution inventory of vehicle emissions in Ecuador using the IVE modelling system, which was developed for its use in third world countries. The required data was collected in several provinces of the country, determining vehicle intensity, driving patterns, departure patterns, environmental variables, and vehicle technologies. To have a greater data representation, vehicles were classified into five categories according to their size, in addition three types of roads were also considered (Highways, Roads and Residential). The database was used to determine the specific power of the engine and "bines", variables that together with the emission factors are part of the calculation of IVE model. Atmospheric pollutants such as CO, VOC's and VOC Evap, NOx, SOx, PM, CO2 and CH4 were also considered, it has been identified that in Ecuador 3.66 million tons of CO were produced in 2015, with trucks representing road transportation being the largest pollutants with approximately 57.2% of the whole total. Through the spatial disaggregation it was possible to identify that the most critical areas, in terms of generation of atmospheric pollutants, are in the most densely populated cities of the country such as Quito and Guayaquil, as well as in areas near seaports and state roads, from 6:00 h, 12:00 h and 18:00 h the hours of the day in which the largest number of emissions are produced. At the end of the study, it was discovered that trucks were the ones that generated the highest emissions of atmospheric pollutants in Ecuador.

Assuntos

Poluentes Atmosféricos , Poluição do Ar , Compostos Orgânicos Voláteis , Humanos , Emissões de Veículos/análise , Equador , Monitoramento Ambiental , Poluentes Atmosféricos/análise , Poluição do Ar/análise

RESUMO

This data article provides an extensive and complete description of the high spatial resolution inventory (HSRI) estimation shown in the article "High resolution inventory of atmospheric emissions from livestock production, agriculture, and biomass burning sectors of Argentina" Puliafito et al. [1], and its comparison with several sectors in Argentina. The dataset provided are high-resolution inventories (0.025° × 0.025° lat/long) for CO2, CH4, N2O and another 8 species from livestock, biomass burning, agriculture and another 12 sectors (based on 2016 year). In addition, we also provide the database for 2014 using the same methodology. The dataset presented are necessary to improve input inventories for air quality models. Also, they are better to inform and guide the stakeholders, in making decisions related to environmental protection and health promotion, as well as assessing the environmental performance in terms of atmospheric emissions of an activity, sector or region in Argentina.

RESUMO

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.