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Climate change is an important driver of the increased spread of dengue from tropical and subtropical regions to temperate areas around the world. Climate variables such as temperature and precipitation influence the dengue vector's biology, physiology, abundance, and life cycle. Thus, an analysis is needed of changes in climate change and their possible relationships with dengue incidence and the growing occurrence of epidemics recorded in recent decades. This study aimed to assess the increasing incidence of dengue driven by climate change at the southern limits of dengue virus transmission in South America. We analyzed the evolution of climatological, epidemiological, and biological variables by comparing a period of time without the presence of dengue cases (1976-1997) to a more recent period of time in which dengue cases and important outbreaks occurred (1998-2020). In our analysis, we consider climate variables associated with temperature and precipitation, epidemiological variables such as the number of reported dengue cases and incidence of dengue, and biological variables such as the optimal temperature ranges for transmission of dengue vector. The presence of dengue cases and epidemic outbreaks are observed to be consistent with positive trends in temperature and anomalies from long-term means. Dengue cases do not seem to be associated with precipitation trends and anomalies. The number of days with optimal temperatures for dengue transmission increased from the period without dengue cases to the period with occurrences of dengue cases. The number of months with optimal transmission temperatures also increased between periods but to a lesser extent. The higher incidence of dengue virus and its expansion to different regions of Argentina seem to be associated with temperature increases in the country during the past two decades. The active surveillance of both the vector and associated arboviruses, together with continued meteorological data collection, will facilitate the assessment and prediction of future epidemics that use trends in the accelerated changes in climate. Such surveillance should go hand in hand with efforts to improve the understanding of the mechanisms driving the geographic expansion of dengue and other arboviruses beyond the current limits. https://doi.org/10.1289/EHP11616.

Narrative evidence contained within historical documents and inscriptions provides an important record of climate variability for periods prior to the onset of systematic meteorological data collection. A common approach used by historical climatologists to convert such qualitative information into continuous quantitative proxy data is through the generation of ordinal-scale climate indices. There is, however, considerable variability in the types of phenomena reconstructed using an index approach and the practice of index development in different parts of the world. This review, written by members of the PAGES (Past Global Changes) CRIAS working group – a collective of climate historians and historical climatologists researching Climate Reconstructions and Impacts from the Archives of Societies – provides the first global synthesis of the use of the index approach in climate reconstruction. We begin by summarising the range of studies that have used indices for climate reconstruction across six continents (Europe, Asia, Africa, the Americas, and Australia) as well as the world's oceans. We then outline the different methods by which indices are developed in each of these regions, including a discussion of the processes adopted to verify and calibrate index series, and the measures used to express confidence and uncertainty. We conclude with a series of recommendations to guide the development of future index-based climate reconstructions to maximise their effectiveness for use by climate modellers and in multiproxy climate reconstructions.

In tropical countries such as Sri Lanka, where leptospirosis—a deadly disease with a high mortality rate—is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was first used with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and an autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR), and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones, and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. The best-fitted SARIMA models in the three zones were different from each other. Despite its known association, rainfall was positively significant in the WZ only at lag 5 ( P = 0.03) but was negatively associated at lag 2 and 3 ( P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL ( P < 0.009) and was negatively associated at lag 1 for the WZ ( P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.

With the rapid development of industry, haze pollution has become an urgent environmental problem. This study innovatively utilizes network-based methods to investigate the spatial correlation effects of haze pollution transmission between urban clusters in the Yangtze River Economic Belt. A spatial correlation network of haze pollution in the Yangtze River Economic Belt was constructed using 328 urban meteorological data collection points as research samples, and its structural characteristics were examined. Main findings are as follows: (1) The spatial correlation network of PM 2.5 in the Yangtze River Economic Belt urban agglomeration exhibits typical network structural characteristics: obvious spatial correlation within the network. (2) Chengdu, Chongqing, Wuhan, Nanchang, Yichang, Changsha and Yueyang are located at the center of the spatial network. They have more receiving and sending relationships. (3) 36 cities can be divided into four types: bilateral overflow, net beneficiary, net overflow and broker. Each type has different functional characteristics and linkage effects in the network. (4) Haze pollution positively correlates with the city’s synergistic development capacity and urbanization rate, the higher the city’s development level and the higher the Urbanization rate, the stronger its haze pollution capacity. This study provides new insights into the study of the spatial correlation and impact of haze pollution.

[...]the traditional method of meteorological data collection and transmission has a large delay in data acquisition due to the conversion of public network and internal network, which affects the timeliness of emergency decision-making. The traditional methods of meteorological data collection and transmission are difficult to meet the needs of modern gency This paper proposes an efficient real-time meteorological data collection and emergency response system based on the Internet of things platform. Traditional methods of meteorological data collection and transmission usually rely on regular or irregular data exchange between observation equipment at fixed stations and central data processing system. In general, the traditional methods of meteorological data collection and transmission face challenges in real-time, reliability and flexibility, especially in response to emergencies, which is difficult to meet the needs of rapid access to accurate meteorological information. [...]it is of great significance to improve the efficiency of real-time collection and transmission of meteorological data by exploring new technologies and solutions, such as the application of Internet of things technology and MQTT protocol[10-11].

The weather station on the Loughborough University campus underwent refurbishment and upgrade in 2007, and this contribution reports on the outcome of 14 subsequent years of meteorological data collection there, before a further episode of upgrading. Data collection is described, with emphasis on the continuity or lack of continuity of the variables monitored. Out of 136 instrument‐years deployment, only 36 are less than 90% complete, and 21 less than 75% complete. Data processing discusses the method of retrieving 0900‐0900 temperature maxima and minima and rainfall totals, to correspond to the standard UK and Ireland Climatological Day. As an independent check on the probable reliability of the campus weather dataset, values are correlated with and regressed against co‐located values extracted from the UK Met Office HadUK‐grid dataset. Campus temperatures are slightly, but consistently, higher than those indicated by HadUK‐grid, while HadUK‐grid rainfall is on average almost 10% higher than that recorded on the campus. Trend‐free statistical relationships between campus and HadUK‐grid data imply that there is unlikely to be any significant temporal bias in the campus dataset. The contribution concludes with a consideration of recent and potential future applications of the dataset. This contribution reports on 14 years' meteorological data collection on the Loughborough University campus, UK. Temperatures are consistently higher than HadUK‐grid, while HadUK‐Grid rainfall is on average almost 110% of that recorded here. However, trend‐free relationships between both datasets imply an absence of significant temporal bias in the campus data.

Objective: The objective of this study is to evaluate the monitoring of Aedes aegypti infestation using the ovitrap method in the city of Toritama – Pernambuco, in the face of COVID-19.   Theoretical Framework: Vector monitoring is extremely important for epidemic control. The most used method in Brazil is the Rapid Index Survey for A. aegypti (LIRAa). Among the existing traps for monitoring A. aegypti, the ovitrap is the most used. With the COVID-19 pandemic and the maintenance of cases of arboviruses in the epidemiological bulletins of this period, it became necessary to emphasize care with arboviruses, highlighting the similarity of symptoms, complications and underreporting, as highly relevant factors.   Method: The research was carried out in the city of Toritama, a municipality located in the countryside of Pernambuco, Brazil, from June 2022 to May 2023. 11 ovitraps were installed in the Center neighborhood of the city. Unstructured interviews, semi-structured observations and meteorological data collection were also carried out. The IDO and IPO were tested using Pearson's correlation. Epidemiological data were obtained from the Epidemiological Bulletins of the Ministry of Health and the Toritama-PE City Hall website.   Results and Discussion: High IPO and IDO values ​​obtained, in contrast to the low record of arbovirus cases in the municipality of Toritama, during the pandemic period studied, reinforces the need to pay attention to the similarities of COVID-19 symptoms.   Research Implications: The research presents the study of monitoring Aedes aegypti infestation using the ovitrap method in the city of Toritama – Pernambuco, during the COVID-19 period.   Originality/Value: The ovitrap method stands out for the more sensitive monitoring of Aedes aegypti and its importance in epidemic or pandemic situations.

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin.

With the continual expansion of global urbanization and population growth, urban energy demands have intensified, and anthropogenic activities have precipitated profound shifts in the global climate. These climatic changes directly alter urban environmental conditions, which in turn exert indirect effects on human physiological function. Consequently, the comfort of outdoor community environments has emerged as a critical metric for assessing the quality of human habitation. Although existing studies have focused on improving singular environmental factors—such as wind or thermal comfort—they often lack an integrated, multi-factor coupling mechanism, and adaptive strategy systems tailored to hot-summer, cold-winter regions remain underdeveloped. This study examines the Minhe Community in Qian’an City to develop a performance evaluation framework for outdoor spaces grounded in subjective comfort and to close the loop from theoretical formulation to empirical validation via an interdisciplinary approach. We first synthesized 25 environmental factors across eight categories—including wind, thermal, and lighting parameters—and applied the Analytic Hierarchy Process (AHP) to establish factor weights, thereby constructing a comprehensive model that encompasses both physiological and psychological requirements. Field surveys, meteorological data collection, and ENVI-met (V5.1.1) microclimate simulations revealed pronounced issues in the community’s wind distribution, thermal comfort, and acoustic environment. In response, we proposed adaptive interventions—such as stratified vegetation design and permeable pavement installations—and validated their efficacy through further simulation. Post-optimization, the community’s overall comfort score increased from 4.64 to 5.62, corresponding to an efficiency improvement of 21.3%. The innovative contributions of this research are threefold: (1) transcending the limitations of single-factor analyses by establishing a multi-dimensional, coupled evaluation framework; (2) integrating AHP with ENVI-met simulation to realize a fully quantified “evaluation–simulation–optimization” workflow; and (3) proposing adaptive strategies with broad applicability for the retrofit of communities in hot-summer, cold-winter climates, thereby offering a practical technical pathway for urban microclimate enhancement.