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In this work, the content of polycyclic aromatic hydrocarbons (PAHs) in total suspended particles and particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) was analyzed using gas chromatography–mass spectrometry. In addition, a sequential chemical analysis of C-rich particles was performed through the parallel coupling of micro-Raman spectroscopy and scanning electron microscopy with X-ray scattering detection. Samples were collected at four sites in the Monterrey metropolitan area, Mexico. A total of 13 PAHs were quantified; indeno(1,2,3-cd)pyrene, chrysene, and benzo(a)anthracene were the most abundant. The total PAH concentrations at the four sampling sites ranged from 1.34 to 8.76 μg/m3. The diagnostic relation of the PAHs indicates that these compounds were emitted by the burning of gasoline and diesel and by the burning of charcoal and biomass. The sequential analysis correlated the morphology and the elemental/molecular composition of the C-rich particles, associated with the PAHs, with their possible emission sources. The estimated lifetime excess cancer risk for inhalation was higher than that established by the World Health Organization, which clearly makes this a potential health risk for the population. [ABSTRACT FROM AUTHOR]

Here, we present an assessment of long-term trends in O3 and odd oxygen (O3 CNO2) at the industrial Monterrey metropolitan area (MMA) in NE Mexico. Diurnal amplitudes in Ox (AVd) are used as a proxy for net O3 production, which is influenced by the NO2 photolysis rate. No significant differences in the AVd are observed between weekends and weekdays, although the largest AVd values are observed at sites downwind of industrial areas. The highest O3 mixing ratios are observed in spring, with minimum values in winter. The largest annual variations in O3 are typically observed downwind of the MMA, with the lowest variations generally recorded in highly populated areas and close to industrial areas. A wind sector analysis of mixing ratios of O3 precursors revealed that the dominant sources of emissions are located in the industrial regions within the MMA and surrounding area. Significant increasing trends in O3 in spring, summer, and autumn are observed depending on site location, with trends in annual averages ranging between 0.19 and 0.33 ppbyr-1. Overall, from 1993 to 2014, within the MMA, O3 has increased at an average rate of 0.22 ppbyr-1 (p < 0:01), which is in marked contrast with the decline of 1.15 ppbyr-1 (p < 0:001) observed in the Mexico City metropolitan area (MCMA) for the same period. No clear trend is observed from 1996 to 2014 within the Guadalajara metropolitan area (GMA). [ABSTRACT FROM AUTHOR]

A six-day tunnel field study was conducted in the city of Monterrey, Mexico, during June 2009 to derive on-road emission factors (EFs) for trace gases and fine particulate matter from the local vehicle fleet. The Loma Larga Tunnel (LLT) is a 532-m-long structure that is mainly used by light-duty gasoline-powered vehicles. It is composed of two independent bores that have a semicircular cross section, 17 m in diameter with a 3.5% slope. During the study, a fleet of 108,569 vehicles with average speeds that ranged from 43 to 76 km/hr was sampled. Ambient air samples were taken inside each bore using 6-L SUMMA-polished canisters and low-volume samplers for the quantification of total nonmethane hydrocarbons (TNMHC) and PM2.5, respectively. The effect of road dust resuspension was considered in the computation of PM2.5 EFs. Additional equipment was used to measure real-time levels of CO2 and NOx; CO EFs were estimated using NOx as a surrogate. TNMHC samples and NOx levels were obtained for 2-hr time periods, while PM2.5 samples and CO2 levels were obtained using 2.5-hr time periods, which included the time periods of the TNMHC and NOx measurements. Estimated EFs for TNMHC, CO, NOx, and PM2.5 were 1.16 ± 0.05, 4.83 ± 2.9, and 0.11 ± 0.07 g/km-veh (2-hr average) and 17.5 ± 5.7 mg/veh-km (2.5-hr average), respectively, while CO2 EFs were 182.7 ± 44 g/km-veh for the 2-hr time periods and 170 ± 22 g/veh-km for the 2.5-hr time periods. The average fuel economy estimated from the field data was 12.3 ± 2.3 km/L. The CO2 and TNMHC EFs (on a mass per distance basis) tended to be higher for traffic moving upslope, while the inverse occurred for the PM2.5 EFs. In comparison to other tunnel studies, the CO2 EFs obtained were similar, the NOx and PM2.5 EFs were lower, and the CO and TNMHC EFs were higher. Implications Mobile source emission factors (EFs) for Mexican cities other than Mexico City are scarce. In Monterrey, Mexico, one of the three major cities in the country, emissions inventories are constructed based on EFs from other locations. However, it is quite relevant to obtain local information to construct reliable inventories. We present what is, to our knowledge, the first tunnel study conducted in a Mexican city other than Mexico City to estimate fleet-average mobile source EFs. This is also the first study that reports PM2.5 EFs derived from a tunnel study in the country. [ABSTRACT FROM PUBLISHER]

Abstract: One of the main sources of air pollution in the Monterrey Metropolitan Area (MMA), Mexico, is vehicle exhaust. In this study, emission factors (EFs) for PM2.5, organic carbon (OC), elemental carbon (EC), trace metals (Na to Pb), cations (Na+, K+, NH4 +) and anions (Cl−, NO3 −, SO4 2−) from mobile sources operating under real-world conditions were determined using the Loma Larga Tunnel (LLT) as the experimental set-up. At each station, low-volume devices were deployed to collect 2.5 h-average PM2.5 samples. From the samples collected, PM2.5 EFs as well as chemical profiles were estimated. During the sampling periods conducted for this study, a fleet of 108,569 vehicles crossed the tunnel with average speeds that ranged from 43 km h−1 to 76 km h−1. 97% of the vehicle samples were gasoline-powered vehicles. Average emission rates of 22.8 ± 7.4 mg veh−1 km−1 and 187 ± 144 mg L−1 for PM2.5 were obtained. Contribution of dust resuspension to the total PM2.5 EFs was analyzed using silicon (Si) as a key marker and main component of fugitive dust. Based on this analysis, we estimated a contribution of dust resuspension of 20%–25%. Trace metals linked to brake-wear emissions indicate a clear distinction between down-slope and up-slope emission profiles. Even though PM2.5 EFs tended to be higher during downhill driving conditions, statistically we found no difference between these two driving modes. This could probably be caused by the cancellation of contributions between species that are preferentially emitted during downhill driving (more metal species and less OC and EC) and those emitted during uphill driving (more OC and EC, and less metals). Vehicular PM2.5 emissions were dominated mainly by OC and EC, species which represented 55.2 ± 2.8% and 16.3 ± 1.6% of the total dust-corrected emitted mass. The OC/EC ratio was 2.85 ± 0.79 and 1.19 ± 0.65 for heavy traffic and moderate traffic conditions, respectively. The results obtained match those of other tunnel studies and can be complementary to the results obtained by other techniques to derive a better emissions inventory for the MMA. [Copyright &y& Elsevier]

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density. [ABSTRACT FROM AUTHOR]

Source attribution of airborne particulate matter (PM) relies on a host of different chemical species. Organic molecular markers are a set of particularly useful marker compounds for estimating source contributions to the fine PM fraction (i.e., PM2.5). Although there are many source apportionment studies based on organic markers, these studies heavily rely on the few studies that report region-specific emission profiles. Source attribution efforts, particularly those conducted in countries with emerging economies, benefit from ad hoc information to conduct the corresponding analyses. In this study, we report organic molecular marker source profiles for PM2.5 emitted from 12 major sources types from five general source categories (meat cooking operations, vehicle exhausts, industries, biomass and trash burning, and urban background) for the Monterrey Metropolitan Area (Mexico). Source emission samples were obtained from a ground-based source-dominated sampling approach. Filter-based instruments were utilized, and the loaded filters were chemically characterized for organic markers by GC-MS. Levoglucosan and cholesterol dominate charbroiled-cooking operation sources while methoxyphenols, PAHs and hopanes dominate open-waste burning, vehicle exhaust and industrial emissions, respectively. A statistical analysis showed values of the Pearson distance < 0.4 and the similarity identity distance > 0.8 in all cases, indicating dissimilar source profiles. This was supported by the coefficient of divergence average values that ranged from 0.62 to 0.72. These profiles could further be utilized in receptor models to conduct source apportionment in regions with similar characteristics and can also be used to develop air pollution abatement strategies. [ABSTRACT FROM AUTHOR]

In this work, the particulate matter (PM) from three different monitoring stations in the Monterrey Metropolitan Area in Mexico were investigated for their compositional, morphological, and optical properties. The main aim of the research was to decipher the different sources of the particles. The methodology involved the ex situ sequential analysis of individual particles by three analytical techniques: scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), polarized light microscopy (PLM), and micro-Raman spectroscopy (MRS). The microanalysis was performed on samples of total suspended particles. Different morphologies were observed for particles rich in the same element, including prismatic, spherical, spheroidal, and irregular morphologies. The sequential microanalysis by SEM-EDS/PLM/MRS revealed that Fe-rich particles with spherical and irregular morphologies were derived from anthopogenic sources, such as emissions from the metallurgical industry and the wear of automobile parts, respectively. In contrast, Fe-rich particles with prismatic morphologies were associated with natural sources. In relation to carbon (C), the methodology was able to distinguish between the C-rich particles that came from different anthopogenic sources—such as the burning of fossil fuels, biomass, or charcoal—and the metallurgical industry. The optical properties of the Si-rich particles depended, to a greater extent, on their chemical composition than on their morphology, which made it possible to quickly and accurately differentiate aluminosilicates from quartz. The methodology demonstrated in this study was useful for performing the speciation of the particles rich in different elements. This differentiation helped to assign their possible emission sources. [ABSTRACT FROM AUTHOR]

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies. [ABSTRACT FROM AUTHOR]

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM2.5, sea salt PM2.5, black carbon (BC), organic carbon (OC), and sulfate (SO42−) reanalysis data were identified as factors that significantly influenced PM2.5 concentrations. The ENN estimated a PM2.5 monthly mean of 25.62 μg m−3 during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m−3; mean absolute error = 1.31 μg m−3. Overall, the PM2.5 levels were higher in winter and spring. The highest PM2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps. [ABSTRACT FROM AUTHOR]

Air pollution caused by fine particles is a problem of great concern in the Monterrey Metropolitan Area (MMA) which is the third largest city and the second most important industrial center in Mexico. In this study, samples of fine particulate matter emissions with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) were collected for 12-hour periods during the spring and fall of 2011 and 2012. Eighty-three samples were analyzed for organic carbon (OC) and elemental carbon (EC). The carbonaceous fraction (OC + EC) accounted for 28–55% of the PM 2.5 mass. The average OC/EC ratios ranged from 7.4 to 12.6, and OC and EC concentrations were statistically significant correlated ( R 2 = 0.81, p < 0.01). The secondary organic aerosol (SOA) contributions were determined using two approaches: the EC tracer method based on a primary OC/EC ratio derived from a tunnel study and the minimum observed OC/EC ratio. SOAs were determined to constitute, on average, 59–87% and 32–45% of the total OC and PM 2.5 , respectively. The relationship between O 3 and wind speed indicated that pollutant levels were influenced by transport events during the spring, while stagnation events predominated during the fall campaigns. Statistically significant correlations were observed between OC and EC and gaseous species (CO, NO x , and SO 2 ), indicating a contribution by combustion of fossil fuels to the carbonaceous material. [ABSTRACT FROM AUTHOR]