The interaction between Urban Heat Island (UHI) and Urban Pollution Island (UPI) is relevant for urban studies as well as for health studies. Both phenomena have not been widely studied in Greater Mexico City (GMC). Therefore, we study the interaction between UHI and UPI in GMC where urban and rural areas are not well defined. Also, long-range transport of pollution affects GMC pollution concentration. We used 5-year data (2015-2019) from weather stations to measure the Air UHI (AUHI) and Landsat 8 data to assess the Surface UHI (SUHI). The Urban Pollution (UP) has been assessed with Particulate Matter PM10 and PM2.5 concentrations measured near the surface by air quality monitoring stations, the Aerosol Optical Depth has been used as a measure of UP. During the dry season (February to May), at night (20:00-07:59 LT), a positive correlation (statistically significant) between the UHI and UP has been found (r=0.39) for the UPI defined with PM2.5 between the urban and rural stations. Whereas a significant correlation (r=0.50) has been found between UPI defined with PM10 between the urban and rural station during the autumn.
The seasonal and diurnal variability of the wind resource in Northern Mexico is examined. Fourteen weather stations were grouped according to the terrain morphology and weather systems that affect the region to evaluate the impact on wind ramps and high wind persistent events. Four areas driven by weather systems seasonality are identified. Wind power ramps and persistent generation events are produced by cold fronts in winter, while mesoscale convective systems and local circulations are dominant in summer. Moreover, the 2013 wind forecast of the Rapid Refresh Model (RAP) and the North American Mesoscale Forecast System (NAM) forecast systems were also assessed. In general, both systems have less ability to predict mesoscale events and local circulations over complex topography, underestimating strong winds and overestimating weak winds. Wind forecast variations in the mesoscale range are smoother than observations due to the effects of spatial and temporal averaging, producing fewer wind power ramps and longer lasting generation events. The study carried out shows the importance of evaluating operational models in terms of wind variability, wind power ramps and persistence events to improve the regional wind forecast. The characteristics of weather systems and topography of Mexico requires model refinements for proper management of the wind resource.
Air pollution can be produced from anthropogenic or natural sources. Most of the policies enacted to improve air quality focus on reducing anthropogenic sources of pollution, but if natural sources increase, then air quality does not improve with these policies. In this chapter, we first define the diurnal and monthly cycle of particulate matter and ozone concentration, depending on the weather, using data from air quality monitoring stations from Greater Mexico City. We then look at a mayor public policy intervention during the COVID-19 pandemic that drastically reduced anthropogenic sources of PM but did not reduce natural sources by doing robust trend analysis on air quality station data. We evaluate the effect of these interventions by looking at national air quality standards and the number of days air pollutants have been within recommended levels. The results show that during lockdown, air quality improved because less anthropogenic sources of PM were active. However, natural sources contributed to air pollution during that time.
The intermittent nature of wind resources is challenging for their integration into the electrical system. The identification of weather systems and the accurate forecast of wind ramps can improve wind-energy management. In this study, extreme wind ramps were characterized at four different geographical sites in terms of duration, persistence, and weather system. Mid-latitude systems are the main cause of wind ramps in Mexico during winter. The associated ramps last around 3 h, but intense winds are sustained for up to 40 h. Storms cause extreme wind ramps in summer due to the downdraft contribution to the wind gust. Those events last about 1 to 3 h. Dynamic downscaling is computationally costly, and statistical techniques can improve wind forecasting. Evaluation of the North American Mesoscale Forecast System (NAM) operational model to simulate wind ramps and two bias-correction methods (simple bias and quantile mapping) was done for two selected sites. The statistical adjustment reduces the excess of no-ramps (≤|0.5| m/s) predicted by NAM compared to observed wind ramps. According to the contingency table-derived indices, the wind-ramp distribution correction with simple bias method or quantile mapping method improves the prediction of positive and negative ramps.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.