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The objective was to analyze how representative tropospheric NO2 column densities are of surface NO2 measurements under different atmospheric stability conditions in the air basin of two border cities: Calexico, United States, and Mexicali, Mexico. NO2 columns were measured by the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite. NO2 concentrations and meteorological parameters were also measured on the surface for comparison. Specifically, the correlations between OMI and surface NO2 concentrations under different atmospheric stability conditions according to the Pasquill‐Gifford (P‐G) and Monin‐Obukhov (M‐O) classification schemes were determined for 2017 and 2018. During the passage of the satellite through the study area (11:00–13:00 UTC−8), unstable conditions were documented in both years. Good correlation was found between the surface NO2 and OMI NO2 column observations in the second semester of each year, particularly under unstable conditions as diagnosed by the P‐G and M‐O schemes applied in the first and second year, respectively. However, a weakening of these conditions occurs during the autumn–winter period. In both cases, the highest determination coefficients were found for Calexico, with values of 0.48 and 0.36 in 2017 and 2018, respectively; for Mexicali, the determination coefficients were 0.23 and 0.35, respectively. Under each atmospheric stability scheme, the mechanical and convective turbulence caused a decreasing trend in wind speed and solar radiation over the course of second semester of 2017 and in friction velocity, temperature, and sensible heat flux over the course of the same period for 2018. The negative trend of these parameters during the analyzed time frames helped to reduce the influence of unstable atmospheric conditions, favoring better correlations between satellite and surface NO2 measurements. The methodology applied and results obtained herein can enable us to better understand the representativeness of OMI NO2 data in arid border zones with extreme meteorological conditions.

Mexico is vulnerable to extreme climatic events; however, their impact is not uniform in all the country. This study presents an analysis of extreme temperatures in 12 Mexican cities, modeled under the assumption of a non-stationary climate. Temporal trends were estimated from an available climatological base of maximum and minimum temperatures with the non-parametric tests of Mann-Kendall and Sen’s slope method, and a generalized extreme value (GEV) distribution was used to model both temperatures. A likelihood ratio test and Akaike and Bayesian information criteria were used to evaluate the optimal model choice with incorporation of a covariate. Using the best model, return levels and confidence intervals for future scenarios were estimated. A trend towards urban warming was detected from both the non-parametric tests and the GEV distribution, although with heterogeneous behavior. In the series of the maximum temperatures, half of the cities analyzed were non-stationary, and of those, the city of Guadalajara, located in the center-west of the country had a negative trend. The trend for minimum temperatures was more uniform, as 90% of the cities were non-stationary with a positive trend, and only 10%, in an urban area to the east of the metropolitan area of the Valley of Mexico (Milpa Alta) and a coastal city of the Gulf of Mexico (Veracruz), showed stationary series. It is therefore concluded that return periods of thermal extremes estimated in a changing climate temporarily showed a significant variation, so statistical modeling must consider this behavior due to its importance for risk assessments and adaptation purposes.

Global studies focusing on climate extremes in urban areas, specifically in Mexico, have not been approached with a sufficient level of detail, despite considerably increased risks for both infrastructure and human society. Based on 14 climate indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) and the application of the nonparametric Mann-Kendall and Sen’s slope tests, the observed trends of climate extremes in 16 urban areas in Mexico from 1980 to 2010 were analyzed. The results show that climate conditions over most cities of Mexico are changing, as indicated by a warming trend during the study period. Significant increases were detected in the annual average maximum temperature (TMX) and the annual average minimum temperature (TMN). There is no clear trend that the warmest days (TXx) are increasing, but the annual number of warm days (TX90p) and summer days (SU) has increased, and the cold days (TX10p) have decreased. Associated with these changes are concomitant decreases in fewer coldest nights (TNn), an increase in the percentage of days with warm nights (TN90p) and tropical nights (TR), and a decrease in the number of frost days (FD). In contrast, the analysis reveals a statistically significant decrease in the mean temperature and the amount of warm days in Mexico City, which could be associated with the increasing trend of atmospheric pollution and the continuous smoke and ash emissions from the volcano Popocatepetl, which disperse incoming solar radiation and reduce radiative forcing. Climate change indices based on daily precipitation data show positive trends in many cities. In general, an increasing trend in very wet days (R95p), a number of very heavy rainfall days ≥ 25 mm (R25), and the annual total wet days (PRCP) were observed. These results highlight the vulnerability of cities confronted with both global and local climate change and the importance of promoting effective local risk reduction measures as part of urban planning for city dwellers.

This article presents a proposal for the implementation of photovoltaic systems in homes located in Mexicali, Mexico. With exhibition of new insulation and different consumption characteristics. The photovoltaic low power system is proposed as a type of electrical supply that helps in reducing the environmental impact of generating energy by burning fossil fuels. A photovoltaic system installed in the city of Yuma, Arizona in the United States, which supplies the electrical needs to a building is selected as a reference for solar resource use. Energy use improvement and consumption costs are calculated, and the equivalent amounts of greenhouse gases, not generated since solar technologies were implemented are determined. Furthermore, comparisons of the solar potential between Yuma and Mexicali show that the Mexican city has a higher annual solar potential, so the applicability of this solar technology is too feasible in Mexicali.

The generalized extreme value distribution is applied in this article to model the statistical behavior of the maximum and minimum temperature distribution tails in four cities of Baja California in northwestern Mexico, using data from 1950–2010. The approach used of the maximum of annual time blocks. Temporal trends were included as covariates in the location parameter (μ), which resulted in significant improvements to the proposed models, particularly for the extreme maximum temperature values in the cities of Mexicali, Tijuana, and Tecate, and the extreme minimum temperature values in Mexicali and Ensenada. These models were used to estimate future probabilities over the next 100 years (2015–2110) for different time periods, and they were compared with changes in the extreme (P90th and P10th) percentiles of maximum and minimum temperature scenarios for a set of six general circulation models under low (RCP4.5) and high (RCP8.5) radiative forcings. By the end of the twenty-first century, the scenarios of the changes in extreme maximum summer temperature are of the same order in both the statistical model and the high radiative scenario (increases of 4–5 °C). The low radiative scenario is more conservative (increases of 2–3 °C). The winter scenario shows that minimum temperatures could be less severe; the temperature increases suggested by the probabilistic model are greater than those projected for the end of the century by the set of global models under RCP4.5 and RCP8.5 scenarios. The likely impacts on the region are discussed.

The waste derived from Electrical and Electronic Equipment (WEEE) is of interest now worldwide, in some countries due to the rapid technological development and in others due to technological dependence through the acquisition of second hand equipment. Therefore it is necessary to implement strategies for the sustainable management of electrical and electronic equipment at the end of its useful life. This paper presents the results of an investigation that was conducted in a city located in the Northwest of Mexico, which seeks to understand the management practices of household WEEE generators. A survey was applied to a representative sample of the population with a confidence level of 90%. Six home appliances were quantified and it was found that approximately 21% of them are disposed directly as part of the household flow of Municipal Solid Waste (MSW), while 79% are storage for a latter sale, repair or reuse. The results obtained support the need to develop evaluations and to implement possible scenarios for the sustainable management of Electrical and Electronic Waste.

The most severe impacts of climate on human society and infrastructure as well as on ecosystems and wildlife arise from the occurrence of extreme weather events such as heat waves, cold spells, floods, droughts and storms. Recent years have seen a number of weather events cause large losses of life as well as a tremendous increase in economic losses. According to the IPCC (2007), an extreme weather event is an event that is rare at a particular place and time of year. Definitions of rare vary, but an extreme weather event would normally be as rare as or rarer than the 10th or 90th percentile of the observed probability density function. Changes in frequency or/and intensity of extreme events can affect not only human health, directly through heat and cold waves and indirectly by floods or pollution episodes, but also for example, on crops or even insurance calculations. Climate extremes associated with temperature (heatwaves) and precipitation (heavy rain, snow events, droughts) can also affect energy consumption, human comfort and tourism and are responsible for a disproportionately large part of climate-related damages (Easterling et al., 2000; Meehl et al., 2000). Extreme weather events recorded in recent years, and associated losses of both, lives and economics goods, has captured the interest of the general public, governments, stakeholders and media. The scientific community has responded to this inquiry and has raised interest in studying with more attention to detail. Our understanding of the mean behavior of climate and its normal variability has been improving significantly during the last decades. In comparison, climatic extreme events have been hard to study and even harder to predict because they are, by definition, rare and obey different statistical laws than averages. In particular, extreme value analysis usually requires estimation of the probability of events that are more extreme than any that have already been observed, and they are linked to small probabilities. Climate extremes can be placed into two broad groups: (i) those based on simple climate statistics, which include extremes such as a very low or very high daily temperature, or heavy daily or monthly rainfall amounts, that occur every year; and (ii) more complex event-driven extremes, examples of which include drought, floods, or hurricanes, which do not necessarily occur every year at a given location. Katz & Brown (1992) first suggested that the sensitivity of extremes to changes in mean climate may be greater than one would assume from simply shifting the location of the climatological distributions. Since then, observations of historical changes as well as future