Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

Tracking fine particles in urban and rural environments using honey bees as biosamplers in Mexico

This work explores the efficiency of honey bees (Apis mellifera) as biosamplers of metal pollution. To understand this, we selected two cities with different urbanization (a medium-sized city and a megacity), and we collected urban dust and honey bees captured during flight. We sampled two villages and a university campus as control areas. The metal content in dust was analyzed by inductively coupled plasma mass spectrometry (ICP-MS). Atomic Force Microscopy (AFM) and Scanning electron microscopy (SEM) were used to investigate the shape and size distribution of the particles, and to characterize the semiquantitative chemical composition of particles adhered to honey bee's wings. Principal Component Analysis (PCA) shows a distinctive urban dust geochemical signature for each city, with component 1 defining V-Cr-Ni-Tl-Pt-Pb-Sb as characteristic of Mexico City and Ce-As-Zr for dust from Hermosillo. Particle count using SEM indicates that 69% and 63.4% of the resuspended dust from Hermosillo and Mexico City, respectively, corresponds to PM2.5. Instead, the particle count measured on the honey bee wings from Hermosillo and Mexico City is mainly PM2.5, 91.4% and 88.9%, respectively. The wings from honey bees collected in the villages and the university campus show much lower particle amounts. AFM-histograms confirmed that the particles identified in Mexico City have even smaller sizes (between 60 and 480 nm) than those in Hermosillo (between 400 and 1400 nm). Particles enriched in As, Zr, and Ce mixed with geogenic elements such as Si, Ca, Mg, K, and Na dominate honey bee' wings collected in Hermosillo. In contrast, those particles collected from Mexico City contain V, Cr, Ni, Tl, Pt, Pb, and Sb. Such results agree with the urban dust data. This work shows that honey bees are suitable biosamplers for the characterization of fine dust fractions by microscopy techniques and reflect the urban pollution of the sites.

Occurrence and characteristics of atmospheric microplastics in Mexico City

While atmospheric microplastics have attracted scientific attention as a significant source of microplastic contamination in the environment, studies in large population centers remain sparse. Here we present the first report on the occurrence and distribution of atmospheric microplastics in Mexico City (Latin America's second most densely populated city), collected using PM₁₀ and PM_2.5 active samplers at seven monitoring stations (urban, residential, and industrial) during the dry and wet seasons of 2020. The results showed that microplastics were detected in all of the samples examined, with mean microplastic concentrations (items m^-3) of 0.205 ± 0.061 and 0.110 ± 0.055 in PM₁₀ and PM_2.5, respectively. The spatial distribution of microplastics showed seasonal variation, with greater abundances in locations closer to industrial and urban centers. There was also a significant difference in microplastic concentrations in PM₁₀ and PM_2.5 between the dry and wet seasons. The mean PM_2.5/PM₁₀ ratio was 0.576, implying that microplastics were partitioned more towards PM_2.5 than PM₁₀ in Mexico City. Fibers were the most prominent shape (>75 %), and blue was the most common color (>60 %). The size characteristics indicated microplastics of varying lengths, ranging from 39 to 5000 μm, with 66 % being <500 μm. Metal contaminants such as aluminum, iron, and titanium were detected using SEM-EDX on randomly selected microplastics. The microplastics were identified as cellophane, polyethylene, polyethylene terephthalate, polyamide, and cellulose (rayon) using ATR-FTIR spectral analysis. Our findings unravel the extent and characteristics of atmospheric microplastics in the Mexico City metropolitan area, which will aid future research to better understand their fate, transport, and potential health risks, demanding more investigations and close monitoring.

A Novel Tree Ensemble Model to Approximate the Generalized Extreme Value Distribution Parameters of the PM2.5 Maxima in the Mexico City Metropolitan Area

We introduce a novel spatial model based on the distribution of generalized extreme values (GEVs) and tree ensemble models to analyze the maximum concentrations levels of particulate matter with a diameter of less than 2.5 microns (PM2.5) in the Mexico City metropolitan area during the period 2003–2021. Spatial trends were modeled through a decision tree in the context of a non-stationary GEV model. We used a tree ensemble model as a predictor of GEV parameters to approximate nonlinear trends. The decision tree was built by using a greedy stagewise approach, the objective function of which was the log-likelihood. We verified the validity of our model by means of the likelihood and Akaike’s information criterion (AIC). The maps of the generalized extreme value parameters on the spatial plane show the existence of differentiated local trends in the extreme values of PM2.5 in the study area. The results indicated strong evidence of an increase in the west–east direction of the study area. A spatial map of risk with maximum concentration levels of PM2.5 in a period of 25 years was built.

Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM_2.5 concentrations at moderate scales. The current study calibrated PM_2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R²), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM_2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R² of 0.74, a low RMSE of 16.29 μg × m^-3, and a small MPE of -0.282 μg × m^-3. Meanwhile, the performance of the RF model in inferring the PM_2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM_2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM_2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM_2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM_2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM_2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM_2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM_2.5 pollution.

A Novel Method for Regional NO2 Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network

Achieving accurate predictions of urban NO₂ concentration is essential for effectively control of air pollution. This paper selected the concentration of NO₂ in Tianjin as the research object, concentrating predicting model based on Discrete Wavelet Transform and Long- and Short-Term Memory network (DWT-LSTM) for predicting daily average NO₂ concentration. Five major atmospheric pollutants, key meteorological data, and historical data were selected as the input indexes, realizing the effective prediction of NO₂ concentration in the next day. Firstly, the input data were decomposed by Discrete Wavelet Transform to increase the data dimension. Furthermore, the LSTM network model was used to learn the features of the decomposed data. Ultimately, Support Vector Regression (SVR), Gated Regression Unit (GRU), and single LSTM model were selected as comparison models, and each performance was evaluated by the Mean Absolute Percentage Error (MAPE). The results show that the DWT-LSTM model constructed in this paper can improve the accuracy and generalization ability of data mining by decomposing the input data into multiple components. Compared with the other three methods, the model structure is more suitable for predicting NO₂ concentration in Tianjin.

Spatio-Temporal Representativeness of Air Quality Monitoring Stations in Mexico City: Implications for Public Health

Assessment of the air quality in metropolitan areas is a major challenge in environmental sciences. Issues related include the distribution of monitoring stations, their spatial range, or missing information. In Mexico City, stations have been located spanning the entire Metropolitan zone for pollutants, such as CO, NO2, O3, SO2, PM2.5, PM10, NO, NO x , and PM CO . A fundamental question is whether the number and location of such stations are adequate to optimally cover the city. By analyzing spatio-temporal correlations for pollutant measurements, we evaluated the distribution and performance of monitoring stations in Mexico City from 2009 to 2018. Based on our analysis, air quality evaluation of those contaminants is adequate to cover the 16 boroughs of Mexico City, with the exception of SO2, since its spatial range is shorter than the one needed to cover the whole surface of the city. We observed that NO and NO x concentrations must be taken into account since their long-range dispersion may have relevant consequences for public health. With this approach, we may be able to propose policy based on systematic criteria to locate new monitoring stations.

Identifying the spatiotemporal dynamic of PM2.5 concentrations at multiple scales using geographically and temporally weighted regression model across China during 2015-2018

Fine particulate matter (PM_2.5) is closely related to the air quality and public health. Numerous models have been introduced to simulate the PM_2.5 concentrations at large scale based on remote sensing and auxiliary data. However, the data precision provided by these models are inadequate for epidemiology and pollutant exposure studies at medium or small scale. The present study aims to calibrate PM_2.5 concentrations at 1 km resolution scale across China during 2015-2018 based on monitoring station data and auxiliary data using a novel geographically and temporally weighted regression model (GTWR). The cross-validation (CV) method and the geographically weighted regression (GWR) model are conducted for validation and cross-comparison. Additionally, the spatial autocorrelation and slope analysis methods are implemented to detect the spatiotemporal dynamic of PM_2.5 concentrations. A sample-based CV of the GTWR model demonstrates an acceptable precision with a coefficient of determination equal to 0.67, a root-mean-square error of 10.32 μg/m³, and a mean prediction error of-6.56 μg/m³. This result proves that the GTWR model can simulate PM_2.5 concentrations at a higher spatial resolution and accuracy across China than some previous models. Besides, the heterogeneity and spatiotemporal dynamic of PM_2.5 concentrations are obvious, that is, the High-High (H-H) agglomeration areas with strong haze pollution were mainly concentrated in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Chengdu-Chongqing (CY), and Guanzhong Plain (GZP). In addition, the PM_2.5 concentrations are undergoing a decreasing trend in most of the study area, and the decrease in the BTH is dramatic. The results of the present study are helpful for calibrating and detecting the spatiotemporal dynamic of PM_2.5 concentrations and useful for the government to make decisions about decreasing haze pollution in urban agglomeration scale.

Influence of Innovative Woodchipper Speed Control Systems on Exhaust Gas Emissions and Fuel Consumption in Urban Areas

This paper discusses the determination of fuel consumption and exhaust gas emissions when shredding branches in urban areas. It aimed to determine the hourly emission of exhaust gases to the atmosphere during such work and to identify the designs that can reduce it. The research was carried out with a cylinder woodchipper driven by a low-power (9.5 kW) combustion engine. There were three configurations of the tested drive unit: The factory setting (A) with a carburettor fuel supply system, modernized by us to include an electronic injection system (B). This system (B) was expanded with an adaptation system patented by the authors (P. 423369), thus creating the third configuration (C). The research was carried out when shredding cherry plum (Prunus cerasifera Ehrh. Beitr. Naturk. 4:17. 1789 (Gartenkalender 4:189-204. 1784)) branches with a diameter of 80 mm, which presented a large load for the machine. The machine was operated by one experienced operator. The average operating conditions during the tests were as follows: Branch delivery frequency of about 4 min−1 and mass flow rate of about 0.72 t h−1. During the tests with the use of PEMS (portable emissions measurement system, here Axion RS from Global MRV), we analyzed the emissions of compounds, such as CO, CO2, HC, and NOx, and determined the fuel consumption based on the carbon balance. The research showed that the use of an injection system (B) reduced fuel consumption from 1.38 to 1.29 l h−1 (by 6.7%) when compared to the carburettor system (A). Modernization of the injection system (B) with an adaptive system (C) reduced fuel consumption from 1.38 to 0.91 l h−1 (by 34%) when compared to the carburettor system (A). An hour of shredding with a cylinder chopper emits the following amounts of flue gases: design A (HC 0.013 kg h−1; CO 0.24 kg h−1; CO2 2.91 kg h−1; NOx 0.0036 kg h−1), design B (HC 0.0061 kg h−1; CO 0.20 kg h−1; CO2 2.77 kg h−1; NOx 0.0038 kg h−1), and design C (HC 0.017 kg h−1; CO 0.22 kg h−1; CO2 1.79 kg h−1; NOx 0.0030 kg h−1). The adaptive system entails significant reductions in non-HC emissions, which indicates that the system needs to be improved with respect to fuel-air mixture control for its enrichment of the low-to-high-speed change. The admissible emission limits for harmful compounds in exhaust gas for the tested group of propulsion units are in accordance with the provisions in force in the European Union from 2019 for the tested propulsion units during operation, with a full CO load about 6100 g h−1 and HC + NOx about 80 g h−1. The tested propulsion units emitted significantly less pollution under real operating conditions (because they did not work under full load throughout the entire test sample).