Guillermo Antonio Baigorria

Background: Urbanization and climate change exacerbate mosquito-borne disease transmission by expanding mosquito habitats in densely populated areas. Traditional mosquito control methods, such as eliminating breeding sites or using insecticides, face challenges in urban settings due to practicality and resistance issues. This study introduces Human-Controlled Breeding Sites (HCBS), a novel, low-cost, and environmentally friendly mosquito control method tailored for urban and suburban households. HCBS provides controlled oviposition sites to attract gravid female mosquitoes, disrupting their reproductive cycle without chemical deterrents. Methods: The HCBS method involves deploying devices designed to mimic natural breeding sites, encouraging mosquitoes to lay eggs in a controlled environment. After 10 days, eggs, larvae, and pupae are collected and destroyed. The HCBS device, fabricated using 3D-printed ABS material in five colors (white, black, red, green, blue), was tested in a semi-controlled laboratory in La Molina, Peru. The experiment evaluated oviposition preferences of Aedes aegypti by counting larvae and pupae across five 10-day cycles, with device colors randomized to minimize bias. Results: HCBS devices successfully attracted gravid Aedes aegypti for oviposition, with green devices showing the highest initial preference (2-17 larvae/pupae per cycle), though larvae were later observed in all colors, indicating color is not essential. The method achieved 100% effectiveness in disrupting the mosquito life cycle by eliminating eggs, larvae, and pupae within the 10-day timeframe. The low-cost design and use of household materials enhance its scalability and community engagement potential. Conclusions: HCBS offers a sustainable alternative to conventional mosquito control by targeting early life stages, reducing populations with minimal environmental impact. Its adaptability to household settings and cost-effectiveness make it suitable for urban and suburban areas. Further research is needed to optimize device design, assess long-term efficacy, and integrate HCBS into disaster risk management systems for broader public health applications, particularly in regions like Peru with documented insecticide resistance.

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.

A set of the North American Regional Climate Change Assessment Program (NARCCAP) regional climate models is used in crop modeling systems to assess economically valuable agricultural production in the southeast United States, where weather/climate exerts strong impact on agriculture. The maize/peanut/cotton yield amounts for the period of 1981–2003 are obtained in a regularly gridded (~20 km) southeast U.S. using (a) observed, (b) a reanalysis, and (c) the NARCCAP Phase I multimodel data set. It is shown that the regional‐climate model‐driven crop yield amounts are better simulated than the reanalysis‐driven ones. Multimodel ensemble methods are then adopted to examine their usefulness in improving the simulation of regional crop yield amounts and are compared to each other. The bias‐corrected or weighted composite methods combine the crop yield ensemble members better than the simple composite method. In general, the weighted ensemble crop yield simulations match marginally better with the observed‐weather‐driven yields compared to those of the other ensemble methods.