PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area

Lima is the capital of the Republic of Peru. It is the most important city in the country and as other Latin America metropolises have multiple problems, including air pollution due to particulate material above air quality standards, emitted by 1.6 million vehicles. The “on-line” coupled model of meteorology and chemistry of transport and meteorological/chemistry, WRF/Chem (Weather and Research Forecasting with Chemistry) has been used in the Lima Metropolitan Area, and validated against data observed at ground level with ten air quality stations of the National Service of Meteorology and Hydrology for the year 2016. The goal of this study was to estimate the concentration of PM2.5 particulate matter in the months of February and July of 2016. In both months, the model satisfactorily predicts temperature and relative humidity. The average observed PM2.5 concentrations in the month of July are higher than in February, probably because the relative humidity in July is greater than the relative humidity in February. In the months of February and July the standard observed deviations of the model have a factor of 2.4 and 3.7 respectively, indicating a greater dispersion in the data of the model. In the month of July, the model captures the characteristics of transport, shows characteristic peaks during peak hours, therefore, the model estimates transport behavior better in July than in February. The quality of the air is strongly influenced by the vehicular transport. The PM2.5 particulate material in February had an average bias How to cite this paper: Reátegui-Romero, W., Sánchez-Ccoyllo, O.R., de Fatima Andrade, M. and Moya-Alvarez, A. (2018) PM2.5 Estimation with the WRF/Chem Model, Produced by Vehicular Flow in the Lima Metropolitan Area. Open Journal of Air Pollution, 7, 215-243. https://doi.org/10.4236/ojap.2018.73011 Received: June 13, 2018 Accepted: August 18, 2018 Published: August 21, 2018 Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access W. Reátegui-Romero et al. DOI: 10.4236/ojap.2018.73011 216 Open Journal of Air Pollution that varied from [−13.2 to 4.4 μg/m] and in July [−9.63 to 11.65 μg/m] and a normalized average bias in February that varied from [−0.68 to 0.43] and in July of [−0.46 to 0.48].


Introduction
Lima is the capital of the Republic of Peru. It is located on the country's central coast, on the shores of the Pacific Ocean at 77˚W and 12˚S. The Lima Metropolitan Area (AML) is the most extensive and populated metropolitan area of Peru, with 2819.3 km 2 [1], a population of 10.4 million inhabitants [2] and 1674,145 million vehicles [3]; (http://www.inei.gob.pe/). Cities with over 10 million inhabitants are considered megacities [4] [5] [6] [7]. These megacities are the engines of growing economies, but are also very large sources of air pollutants and climate-forcing agents [8]. Uncontrolled urban sprawl has led to rising environmental problems due to high traffic volume, irregular industry, etc. [5]. The growing problems of congestion, accidents, and lack of security are also worrisome. Yet transportation is also a critical enabler of economic activity and beneficial social interactions [9]. Transportation is a major source of air pollution in many cities [9] [10], especially in developing countries [9]. Air quality at urban background sites is strongly influenced by road traffic emissions, which is the most important emission source concerning its contribution to ambient PM levels [11] [12]. Emitted primary and subsequently formed secondary gas-or particulate-phase pollutants [13], cause substantial health problems especially in megacities with rapidly growing industry and low pollution control [7] [14].
Particles in the atmosphere arise from natural sources, such as windborne dust, sea spray, and volcanoes, and from anthropogenic activities, such as fuel combustion. Whereas an aerosol is technically defined as a suspension of fine solid or liquid particles in a gas [15], atmospheric particulate matter (PM) is one of the primary concerns in megacities, due to their association with health effects and environment problems [16] [17]. Particulate air pollution is a mixture of solid particles and liquid droplets that vary in size, composition, and origin. Only very small particles can be inhaled into the lungs, inhalable particles include particles with an aerodynamic diameter of less than 10 µm, and fine particles; air pollution includes particles with an aerodynamic diameter equal to or below 2.5 µm [16] [18]. Deterioration in urban environmental conditions can have serious effects on human health and welfare, particularly for the poor [9]. Epidemiological studies suggest that exposure to (PM2.5 and PM10) atmospheric particulate matter can cause adverse effects, including coughing, respiratory stress in asthmatics, and reduced lung function [17], bronchitis, and conjunctivitis [19]. These studies have also shown that air pollution exposure is related with general morbidity and mortality due to respiratory and cardiovascular diseases [18] [19].
The ambient air in Peru is contaminated at a high level compared to other Latin American countries, according to a report by the World Health Organization (WHO) [20]. Lima is the only municipality reporting to the World Health Organization and has high levels of particulate matter contributing to poor air quality. Short term symptoms resulting from exposure to air pollution include itchy eyes, nose and throat, wheezing, coughing, shortness of breath, chest pain, headaches, nausea, and upper respiratory infections (bronchitis and pneumonia). It also exacerbates asthma and emphysema. Long term effects include lung cancer, cardiovascular disease, chronic respiratory illness, and developing allergies. Air pollution is also associated with heart attacks and strokes [21]. A study that relates the impact of vehicle flow with adolescent asthma in the city of Lima can be consulted in [22], while in [23], some Peruvian cities with PM air pollution problems are indicated. Problems of indoor air pollution in the periurban community of Lima by vehicular transport can be consulted in [24]. Deficiencies in public transport, as well as air pollution and proposals for improvements for transportation in Lima can be consulted in [25]. The poor air quality in the AML and its relationship with human deaths and other health problems that affect the population due to PM and other air pollutants, can be reviewed in [26] [27], and [28]. The goal of this study was to estimate the PM2.5 particulate matter concentration in the Metropolitan Area of Lima (AML) using the Eulerian WRF-Chem Model, in the months of February (summer) and July (winter) and was validated with measurements at ground level in the ten air quality sta-  [29]. The MOS was defined by [30] as a multiple linear regression technique in which predicands (observed data) are statistically related to one or more predictors (forecasts from a numerical weather prediction (NWP) model).

Study Area
The study area corresponds to the AML, that is located at coordinates (Longitude: 77˚1'41.66W, Latitude: S12˚2'35.45S). The air quality data was provided by the ten monitoring stations of the National Service of Meteorology and Hydrology of Peru (SENAMHI). In Lima, climate is very peculiar: it is a subtropical desert climate, with a warm season from December to April, and a cool, humid, and cloudy season from June to October, with May and November as transition Figure 1 shows the location of the air quality stations on AML and Table 1 indicates the coordinates of these stations of the National Service of Meteorology and Hydrology of Peru (SENAMHI).

Observed Data
The equipment used by the National Meteorology and Hydrology Service of Pe-  TEOM 1405 Monitor is a true gravimetric instrument that draws (then heats) ambient air through a filter at constant flow rate, continuously weighing the filter and calculating near real-time mass concentrations of particulate matter".
"The tapered element at the heart of the mass detection system is a hollow tube, clamped on one end and frees to oscillate at the other. An exchangeable TEOM filter cartridge is placed over the tip of the free end. The sample stream is drawn through this filter, and then down the tapered element. This flow is maintained at a constant volume by a mass flow controller that is corrected for local temperature and barometric pressure" [32]. In this study the TEOM 1405 monitor was used to measure the mass concentration of PM10. "The Model 5014i uses the radiometric principle of beta attenuation through a known area on a fibrous filter tape to continuously detect the mass of deposited ambient particles. Additionally, the Model 5014i measures alpha particle emissions directly from the ambient aerosol being sampled and excludes negative mass artifacts from the daughter nuclides of radon gas decay to achieve a refined mass measurement. Simultaneous refined mass measurements of sampled particulate on the filter tape and sample volume measurement provide a continuous concentration measurement of ambient particulate concentration" [33].

Model of Anthropogenic Emissions in the AML
Because on-road vehicles are the most important sources of air pollution in AML, according to DIGESA, more than 78% of pollutant emissions are generated by vehicular emissions; the anthropogenic emissions of trace gases and particles in 5 km modeling domains were considered to include emissions only coming from on-road vehicles through the use of a vehicular emission model [34]. Then, vehicular emission in the AML for the WRF-Chem Model was estimated using a Vehicular Emission Model-VEM developed by the IAG-USP's Laboratory of Atmospheric Processes-LAPAt [34]. "This VEM model does not include point sources nor biogenic sources, and considers the number of vehicles, vehicular emissions factors, and average driving kilometers for vehicle per day as basic parameters for the calculations of exhaust and evaporative emissions considering different vehicles types and different fuel types" [34]. "For the spatial distribution of air pollution emissions, it is assumed that the vehicles within the modeling domain were distributed proportional to the road length in each grid cell" [12] [13] [35]. "Road length was calculated as the sum of five types of road (motorway, trunk, primary, secondary and tertiary) within each grid cell" [36]. "The road map is available on the Open Street Map project and extracted from the Geofabrik's free download server (http://download.geofabrik.de/)" [36].

Description and Configuration of the WRF/Chem Model
WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with chemistry [37]. The model simulates the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with meteorology. The model is used for investigation of regional-scale air quality, field program analysis, and cloud-scale interactions between clouds and chemistry (https://www2.acom.ucar.edu/wrf-chem); WRF is non-hydrostatic [38] [39], with several dynamic cores as well as many different choices for physical parameterizations to represent processes that cannot be resolved by the model [37].
The WRF physics and chemical options fall into several categories, each containing several choices. The physics categories are: microphysics, cumulus parameterization, planetary boundary layer (PBL), land-surface model, atmospheric radiation, and diffusion [40]. The chemical categories are: several choices for gas-phase chemical mechanisms, photolysis schemes, aerosol schemes etc. [41]. To know which organizations participated in the design of the model, you can consult [42] [43]. For the implementation of WRF/Chem, the initial and frontier conditions of the Global Prediction System (GFS) were used (see Table   2). The predicted data were used for each day at 00:00, 06:00, 12:00 and 18:00  this study over AML [35]. The physical and chemical parameterization schemes used in this study are shown in Table 3. Details can be found in [35] [40].

Statistical Models and Improvement of the WRF-Chem Prognosis with MOS
The mean bias (MB), normalized mean bias (NMB), and RMSE (Root Mean Squared Error) were used to evaluate the model performance in simulating aerosols [53] [54] [55].
( ) where Xoi and Ypi are the average hourly observed and predicted data respectively. In this study, a linear regression with a linear function was used to calcu-    Figure 3 shows the temperature field for the region, clearly marking a high thermal gradient between the coast and the mountain range. In this case, the temperatures over the AML in February (summer) vary between 20˚C and 21˚C, higher than in July (winter), below 19 ˚C. Figure 4 shows the average moisture values in the AML. In February and July the values oscillate around 93% and 82% respectively.  February has a better performance than in July, which is corroborated by the statistical parameters with values closer to zero that are shown in

Statistical Parameters MB, NMB and RMSE in February and
July at the 10 Stations

PM2.5 Spatial Distribution Characteristics
For the month of July, Figure 7 shows the hourly profiles of average PM2.    [60].
For the month of February Figure 8 shows     [60].
For the month of February and July Figure 9 shows the hourly profiles of av-  [60]. In this case the model also underesti- At the STA station ( Figure 10) [65]. As expected, the MOS improves the predicted values and has a better performance.
At the CRB station ( Figure 11), in February, the mean hourly PM2.  The improved forecast has the lowest standard deviation, which means less dispersion. That is, a greater uniformity in the data. In studies such as that of [38] the average values observed and predicted were 12.  This parameter in the studies carried out in the summer season by [65] and [60] were −31.84% and −18.5% respectively. The mean normalized bias (NMB) in    [73].
At the SJL station ( Figure 16) [64], values were 13.73 and 11.80 μg/m 3 respectively, with a factor of 0.86, and in the study of [72], they were 42.

Conclusions
Some possible reasons why a numerical model of time underestimates or overestimates the measured concentrations of particulate matter are: 1) There is uncertainty in the emissions inventory. At this point there is a consensus, as many investigations mention it. Comparisons of emission inventories have revealed large differences in emission estimates, finding that differences for example in primary organic carbon emissions can be as high as 140% [78], the emissions inventory can introduce errors of the order of 40 up to 70% in PM [78]. 2) The WRF/Chem, being a complex numerical model of time, requires simplifications in its parameterizations in order to evaluate the different physical and chemical parameters, and the finite difference method is the most used. Therefore, the model naturally has uncertainties. Different types of uncertainties are mentioned in [79].
3) The biases depend on several factors such as the inventory of emissions used, the horizontal resolution, and selected parameterizations [38] [80].
1) From the analysis of the meteorological parameters, temperature and relative humidity, the model in February and July satisfactorily simulated these parameters.
2) The average PM2.5 concentrations observed in the month of July are higher than in February, probably because the relative humidity in July 2016 was higher than the relative humidity in February 2016.
3) The standard deviations of PM2.5 concentrations observed in February are higher than in July, except for the CRB and SBJ stations, which is indicated by a greater dispersion of the observed data.