Source apportionment of ambient pollution levels in Guayaquil, Ecuador.

In this study, the relative contributions of main emission sources to the typical ambient concentrations of key pollutants, such as sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5) in Guayaquil, Ecuador, were investigated. A previous urban emissions inventory for mobile sources was expanded to include other transportation means and main industrial activities using the EMEP/EEA methodology to achieve this objective. The WRF/CALMET/CALPUFF modeling system was used to simulate the annual spatiotemporal distribution of air pollution in the city. According to the model, NO2 concentrations exceed the yearly value and 1-h Ecuadorian standards (40 and 200 µg/m3) in 1 % and 6 % of the cells of the modeling domain, respectively. These hotspots related to local sources were located in the northwest center of the city. The contributions of the manufacturing sector, thermal power plants, ports, airports, and road traffic were assessed individually, and the results indicated that air quality in the study area was strongly dominated by road traffic. The contributions of NO2, CO, PM10, and PM2.5 at the city level reached 76 %, 96 %, 90 %, and 92 % of the annual mean, respectively. In the case of SO2, the manufacturing sector made the most significant contribution (75 %), followed by thermal power plants (16 %). Furthermore, an analysis at 14 specific locations across Guayaquil identified spatial variations that may support the design and development of an air quality monitoring network for the city.

Evaluation of the danger of a tailings pile belonging to an active mine through its characterization and a dispersion model.

Mining is one of the principal economic activities in Mexico, which in addition to bringing benefits to the population, causes health and environmental problems. This activity produces a lot of wastes, but the main one is tailings. In Mexico, these wastes are disposed of in the open air, and there is no control over them, so the particles of these wastes are dispersed by wind currents to the surrounding population. In this research, tailings were characterized, finding in them particles smaller than 100 microns; in this way, tailings can enter into the respiratory system and hence can cause diseases. Furthermore, it is important to identify the toxic components. The present work does not have previous research in Mexico, and it shows a qualitative characterization of the tailings from an active mine using different analytical techniques. In addition to the data obtained from the characterization of the tailings, as well as the concentration of the toxic elements found, which were Pb and As, a dispersal model was generated and used to estimate the concentration of particles in the wind generated at the studied area. The air quality model used in this research is AERMOD, where it uses emission factors and available databases provided by Environmental Protection Agency (USEPA); Moreover, the model was coupled with meteorological information from the latest generation WRF model. The modeling results estimated that the dispersion of particles from the tailings dam can contribute up to 10.15 µg/m3 of PM10 to the air quality of the site, which, according to the characterization of the samples obtained, could be dangerous for human health and can be estimated up to a concentration of 0.04 µg/m3 of Pb and 10.90 ng/m3 of As. It is very important to make this kind of research to know the risk which people around this disposal sites are exposed to.

An empirical and modelling approach to the evaluation of cruise ships' influence on air quality: The case of La Paz, Mexico.

Maritime activity has diverse environmental consequences impacts in port areas, especially for air quality, and the post-COVID-19 cruise tourism market's potential to recover and grow is causing new environmental concerns in expanding port cities. This research proposes an empirical and modelling approach for the evaluation of cruise ships' influence on air quality concerning NO2 and SO2 in the city of La Paz (Mexico) using indirect measurements. EPA emission factors and the AERMOD modelling system coupled to WRF were used to model dispersions, while street-level mobile monitoring data of air quality from two days of 2018 were used and processed using a radial base function interpolator. The local differential Moran's Index was estimated at the intersection level using both datasets and a co-location clustering analysis was performed to address spatial constancy and to identify the pollution levels. The modelled results showed that cruise ships' impact on air quality had maximum values of 13.66 µg/m3 for NO2 and 15.71 µg/m3 for SO2, while background concentrations of 8.80 for NOx and 0.05 for SOx (µg/m3) were found by analysing the LISA index values for intersections not influenced by port pollution. This paper brings insights to the use of hybrid methodologies as an approach to studying the influence of multiple-source pollutants on air quality in contexts totally devoid of environmental data.

Assessment of meteorological settings on air quality modeling system-a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil.

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO2 dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO2 concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Simulating PM2.5 Concentrations during New Year in Cuenca, Ecuador: Effects of Advancing the Time of Burning Activities.

Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m-3 (maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m-3, the current World Health Organization guideline. We estimated the corresponding PM2.5 emissions and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%, or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM2.5 emissions on 31 December.

Socioeconomic development role in hospitalization related to air pollution and meteorology: A study case in southern Brazil.

Air pollution is one of the foremost environmental threats to human health. However, the meteorological and social factors that lead to respiratory and cardiovascular diseases have not been fully elucidated. In this study, we use Principal Component Analysis and Generalized Linear Model (PCA-GLM) to investigate the combined effect of socioeconomic development and air pollution on cardiorespiratory hospitalization in southern Brazil. This region has the highest rates of hospitalization by cardiorespiratory diseases in the country. We analyze three main sources of data: (i) air pollutants density from TROPOMI/Sentinel-5p satellite; (ii) temperature, humidity, and planetary boundary layer height (PBLH) modeled with the Weather Research Forecast model; and (iii) hospitalization by cardiorespiratory diseases obtained from the Brazilian National Health System. We estimate the Relative Risk (RR) using the PCA-GLM coefficients and interquartile variations of air pollutants density and meteorological parameters. Our results show that the population living in colder and drier municipalities is more prone to cardiorespiratory hospitalization. Regarding respiratory hospitalization, municipalities with lower socioeconomic development are more sensitive to meteorology and pollution variability than highly developed ones. In less developed municipalities, we observe the highest rates of cardiorespiratory hospitalization even if air pollution is low, which we interpret in terms of higher vulnerability. The RR analysis suggests that air pollution is an important environmental risk to cardiovascular diseases and respiratory diseases is more sensitive to air pollution and meteorology than cardiovascular ones. Our findings corroborate the mounting evidence that social vulnerability is a significant factor affecting the increase of cardiorespiratory hospitalization in the world.

Measured and forecasted weather and power dataset for management of an island and grid-connected microgrid.

This article presents the weather and power data files from renewable sources used to solve the economic dispatch problem of a microgrid that operates in the isolated and grid-connected modes. Methodology is used in the research article "Management of an island and grid-connected microgrid using hybrid economic model predictive control with weather data" (Silva et al., 2020). Automatic stations located in the Brazil's south and northeast furnished the weather data (global horizontal irradiance, temperature, and wind speed). A script generates files containing weather forecasts from one-day ahead using the geographical coordinates of the weather stations. Hybrid models, characterized by real and binary variables, use the weather forecasting data to calculate the photovoltaic and wind power forecasts. A microgrid management algorithm uses these forecasts to solve the optimal economic dispatch problem. This data-in-brief paper presents five datasets for each weather station: (i) Weather dataset downloaded from the website of the National Meteorological Institute, (ii) Weather research and forecasting (WRF) dataset derived from the raw data generated by the weather research and forecasting model, (iii) Weather dataset that joins the forecasted data with the measured data in a single file, (iv) Handled dataset that treats some gaps in the weather dataset and converts it to other formats, (v) Files containing only the temperature, global horizontal irradiance, and wind speed data, (vi) Files containing the measured and forecasted wind and solar power.

Dataset for evaluating WRF-Chem sensitivity to biogenic emission inventories in a tropical region. Global online model (MEGAN) vs local offline model (BIGA).

This article presents a dataset comparing emissions of Biogenic Volatile Organic Compounds (BVOC) in a zone of complex topography in the tropical Andes, which presents elevations ranging from 250 to more than 4000 m above sea level in a radius of only 50 km. Two approximations were evaluated, (1) online with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) coupled with the Weather Research and Forecast model with Chemistry (WRF-Chem) and (2) offline applying the Biogenic Altitudinal Gradient Model (BIGA). Modeled concentrations of pollutants (mainly isoprene and tropospheric ozone) were obtained with WRF-Chem employing the biogenic emission models mentioned previously. This information identified areas where BVOC emissions vary significantly, comparing the global emission inventory (MEGAN) and the local inventory (BIGA). Re-evaluation of the emission factors and land cover assigned to those areas in the global online biogenic models should be considered in order to reduce the uncertainty in the values. In addition, the dataset shows the impact of the biogenic emission inventories on the air quality simulations on a tropical high mountain area, where vegetation is diverse, and the altitudinal changes influence meteorological variables.

Assessment of odour emissions by the use of a dispersion model in the context of the proposed new law in Chile.

Chile is looking to define a regulatory framework for the odour emissions of various critical industrial activities. One of these is the sanitary sector, with 300 wastewater treatment plants (WWTP). The basis currently used by the Chilean environmental authority to assess odours is the set of odour emission factors (OEF) taken from the Dutch standard. The aim of this study was to compare these, used as a national reference, with our own OEF calculated from measurements using dynamic olfactometry of 41 WWTP. The dependence of OEF on operational variables such as flow rate and BOD5 was analysed in different plant processes. The current regulations were assessed under the two OEF scenarios for the 95th, 98th and 99.9th percentiles in the Temuco WWTP, using the WRF-CALPUFF modelling protocol. The OEF values of the emission sources showed no strong correlation with operating variables like BOD5 and wastewater flow rates in all plant sections. Our OEF values based on real measurements presented significant differences from the Dutch reference OEF, of the order of 6 UOe/m2/s. The odour emitting-units with the largest differences were the pre-treatment units, flow-splitting chamber and most units of the sludge processing sections. These new OEF offer an alternative paradigm for measuring emissions and an incentive to more accurate calculation of the emissions in critical units such as sludge treatment lines. When the WWTP studied in Temuco was assessed using the OEF calculated in this study, a difference of 1041 OUe/s was found above the odours emissions calculated using the Dutch reference database. Using the Dutch OEF, the odour immission concentrations at nearby receptors were not exceeded for the 95th and 98th percentiles; this might result in deficient environmental assessment under current Chilean laws. We therefore recommend that Chilean institutions should assess projects using the OEF calculated in this study.

Simulation of the Movement of a Locust Swarm in Argentina in 2020.

This work presents a simulation for the trajectory of a locust swarm of the species Schistocerca cancellata (Audinet-Serville), between May 22nd and July 29th, 2020, in Argentina. To obtain the directions, temperature, and intensities of the wind, used to determine the daily traveled distance of the insects, the data of weather forecast from Weather Research and Forecasting (WRF) model are used. A statistical analysis shows the effectiveness of the forecast model used in comparison with the real data given by SENASA, which provides latitude and longitude coordinates for the cited period. The results found for the movement of the cloud were satisfactory, they matched with the real data, identifying that temperature and wind speed have a great influence on the movement of locust swarms. The methodology used allows monitoring in real-time their movement, predicting the trajectory and making it possible to plan actions by government control agencies with pesticides in convenient areas.

The Summer 2019-2020 Wildfires in East Coast Australia and Their Impacts on Air Quality and Health in New South Wales, Australia.

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive character of the wildfires caused smoke pollutants to be transported not only to New Zealand, but also across the Pacific Ocean to South America. At the peak of the wildfires, smoke plumes were injected into the stratosphere at a height of up to 25 km and hence transported across the globe. The meteorological and air quality Weather Research and Forecasting with Chemistry (WRF-Chem) model is used together with the air quality monitoring data collected during the bushfire period and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites to determine the extent of the wildfires, the pollutant transport and their impacts on air quality and health of the exposed population in NSW. The results showed that the WRF-Chem model using Fire Emission Inventory (FINN) from National Center for Atmospheric Research (NCAR) to simulate the dispersion and transport of pollutants from wildfires predicted the daily concentration of PM2.5 having the correlation (R2) and index of agreement (IOA) from 0.6 to 0.75 and 0.61 to 0.86, respectively, when compared with the ground-based data. The impact on health endpoints such as mortality and respiratory and cardiovascular diseases hospitalizations across the modelling domain was then estimated. The estimated health impact on each of the Australian Bureau of Statistics (ABS) census districts (SA4) of New South Wales was calculated based on epidemiological assumptions of the impact function and incidence rate data from the 2016 ABS and NSW Department of Health statistical health records. Summing up all SA4 census district results over NSW, we estimated that there were 247 (CI: 89, 409) premature deaths, 437 (CI: 81, 984) cardiovascular diseases hospitalizations and 1535 (CI: 493, 2087) respiratory diseases hospitalizations in NSW over the period from 1 November 2019 to 8 January 2020. The results are comparable with a previous study based only on observation data, but the results in this study provide much more spatially and temporally detailed data with regard to the health impact from the summer 2019-2020 wildfires.

Implementation and evaluation of WRF simulation over a city with complex terrain using Alos-Palsar 0.4 s topography.

Air quality modeling requires an accurate representation of meteorology, and in cities with complex topography, the performance of meteorological modeling can be improved by using an alternative global digital elevation model (GDEM) such as Alos-Palsar 0.4 s instead of the default elevation data. Bogotá is a city with complex topography geographically located over the Andes Mountains at 2600 m.a.s.l. A reliable meteorological simulation model is critical for performing a suitable air quality modeling in any case of study. Previous researches have been developed using the standard Weather Research and Forecast (WRF) topography (GTOPO 30 s). These studies have been developed with different configurations for the representation of meteorology. The aim of this study is to evaluate Alos-Palsar 0.4 s topography with WRF, and two domain configurations with horizontal spatial resolutions up to 1000 m, to establish a reliable and accurate way to simulate the meteorology in the city of Bogotá. The evaluation quantitative parameters: IOA, r (Pearson), RMSE, MGE, and MB were calculated for the quantitative evaluation of temperature, relative humidity, wind speed, wind direction, and solar radiation. An additional evaluation using Taylor diagrams was performed. Spatial differences were identified in the same locations as well the differences between the elevation from Alos-Palsar 0.4 s and GTOPO30. The results and evaluation suggest that simulations based on Alos-Palsar 0.4 s topography lead to a significant improvement in the meteorology representation by WRF in a region with complex topography such as Bogotá, Colombia.

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

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.

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.

Validation of PM10 and PM2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM.

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM10 and PM2.5. Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 µ g/m3), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the corrective alert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM10 and PM2.5 concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM10 and PM2.5 as a tool for an early alert system (EAS) at least 24 h in advance.

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 the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 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 5557 municipalities, with 1-km2 grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (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.

Analog Site Experiment in the High Andes-Atacama Region: Surface Energy Budget Components on Ojos del Salado from Field Measurements and WRF Simulations.

Remote sensing data are abundant, whereas surface in situ verification of atmospheric conditions is rare on Mars. Earth-based analogs could help gain an understanding of soil and atmospheric processes on Mars and refine existing models. In this work, we evaluate the applicability of the Weather Research and Forecasting (WRF) model against measurements from the Mars analog High Andes-Atacama Desert. Validation focuses on the surface conditions and on the surface energy budget. Measurements show that the average daily net radiation, global radiation, and latent heat flux amount to 131, 273, and about 10 W/m2, respectively, indicating extremely dry atmospheric conditions. Dynamically, the effect of topography is also well simulated. One of the main modeling problems is the inaccurate initial soil and surface conditions in the area. Correction of soil moisture based on in situ and satellite soil moisture measurements, as well as the removal of snow coverage, reduced the surface skin temperature root mean square error from 9.8°C to 4.3°C. The model, however, has shortcomings when soil condition modeling is considered. Sensible heat flux estimations are on par with the measurements (daily maxima around 500 W/m2), but surface soil heat flux is greatly overestimated (by 150-500 W/m2). Soil temperature and soil moisture diurnal variations are inconsistent with the measurements, partially due to the lack of water vapor representation in soil calculations.

Short-Term Responses of Air Quality to Changes in Emissions under the Representative Concentration Pathway 4.5 Scenario over Brazil.

Brazil, one of the world's fastest-growing economies, is the fifth most populous country and is experiencing accelerated urbanization. This combination of factors causes an increase in urban population that is exposed to poor air quality, leading to public health burdens. In this work, the Weather Research and Forecasting Model with Chemistry is applied to simulate air quality over Brazil for a short time period under three future emission scenarios, including current legislation (CLE), mitigation scenario (MIT), and maximum feasible reduction (MFR) under the Representative Concentration Pathway 4.5 (RCP4.5), which is a climate change scenario under which radiative forcing of greenhouse gases (GHGs) reach 4.5 W m-2 by 2100. The main objective of this study is to determine the sensitivity of the concentrations of ozone (O3) and particulate matter with aerodynamic diameter 2.5 µm or less (PM2.5) to changes in emissions under these emission scenarios and to determine the signal and spatial patterns of these changes for Brazil. The model is evaluated with observations and shows reasonably good agreement. The MFR scenario leads to a reduction of 3% and 75% for O3 and PM2.5 respectively, considering the average of grid cells within Brazil, whereas the CLE scenario leads to an increase of 1% and 11% for O3 and PM2.5 respectively, concentrated near urban centers. These results indicate that of the three emission control scenarios, the CLE leads to poor air quality, while the MFR scenario leads to the maximum improvement in air quality. To the best of our knowledge, this work is the first to investigate the responses of air quality to changes in emissions under these emission scenarios for Brazil. The results shed light on the linkage between changes of emissions and air quality.

Developing an Advanced PM2.5 Exposure Model in Lima, Peru.

It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 µg/m3). Mean PM2.5 for ground measurements was 24.7 µg/m3 while mean estimated PM2.5 was 24.9 µg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 µg/m3 (Std.Dev. = 5.97 µg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.

Atmospheric dispersion of methane emissions from sugarcane burning in Mexico.

Methane is a potent greenhouse gas whose atmospheric dispersion may have different implications at distinct scales. One significant contributor to methane emissions is sugarcane farming in tropical areas like in Mexico, which has the sixth highest production level in the world. A consequence of the industrial use of this resource is that sugarcane preharvest burning emits large quantities of methane and other pollutants. The objective of this research is to estimate the methane emissions by sugarcane burning and to analyze their atmospheric dispersion under the influence of meteorological parameters, according to different concentration scenarios generated during a period. The methane emissions were investigated using the methodology of Seiler and Crutzen, based on the stage production during the harvest periods of 2011/2012, 2012/2013 and 2013/2014. Average of total emissions (1.4 × 103 Mg) at the national level was comparable in magnitude to those of other relevant sugarcane-producing countries such as India and Brazil. Satellite images and statistical methods were used to validate the spatial distribution of methane, which was obtained with the WRF model. The results show a dominant wind circulation pattern toward the east in the San Luis Potosi area, to the west in Jalisco, and the north in Tabasco. In the first two areas, wind convergence at a certain height causes a downward flow, preventing methane dispersion. The concentrations in these areas varied from 9.22 × 10-5 to 1.22 × 102 ppmv and 32 × 10-5 to 2.36 × 102 ppmv, respectively. Wind conditions in Tabasco contributed to high dispersion and low concentrations of methane, varying from 8.74 × 105 to 0.33 × 102 ppmv. Methane is a potent greenhouse gas for which it is essential to study and understand their dispersion at different geographic locations and atmospheric conditions.