RESUMEN
Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.
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Ciencia Ciudadana , Monitoreo del Ambiente , Tiempo (Meteorología) , Ciencia Ciudadana/métodos , Humanos , Monitoreo del Ambiente/métodos , Meteorología/métodos , Participación de la ComunidadRESUMEN
O Programa em Saúde Ambiental relacionado a populações expostas à poluição do ar do Município de São Paulo (VIGIAR) tem por objetivo desenvolver ações de vigilância em saúde ambiental, para populações expostas aos poluentes atmosféricos, de forma a orientar medidas de prevenção, promoção da saúde e de atenção integral, conforme preconizado pelo Sistema Único de Saúde (SUS).
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Meteorología , Contaminantes AtmosféricosRESUMEN
It is significant to systematically quantify the propagation thresholds of meteorological drought to different levels of agricultural drought in karst areas, and revealit's the propagation driving mechanisms. This can guide early warning and fine management of agricultural drought. In this study,we selected Guizhou Province as an example. The standardized precipitation evapotranspiration index (SPEI) and standardized soil moisture index (SSI) were used to characterize meteorological and agricultural drought. The run theory was used to identify, merge and eliminate drought events. The maximum correlation coefficient was used to capture the propagation time of meteorological-agricultural drought. The regression models were used to quantify the propagation intensity threshold from meteorological drought to different levels of agricultural drought. Finally, the propagation threshold driving mechanism was explored using geographical detectors. The results show that: (1) in terms of temporal variations during the past 21 years, regional meteorological drought had a shorter duration and a higher intensity than agricultural drought, Particularly, 2011 was a year of severe drought, and agricultural drought was significantly alleviated after 2014. (2) In terms of spatial variations, the "long duration area" of meteorological drought duration showed an "S" shaped distribution in the northeast, and the "short duration area" showed a point-like distribution. The overall duration of agricultural drought showed a spatial distribution of northeast to "medium-high in the northeast and low in the southwest. (3) The drought propagation time showed an alternating distribution of "valley-peak-valley-peak" from southeast to northwest. In terms of propagation intensity thresholds, light drought showed an overall spatial distribution of high in the east and low in the west. Moderate, severe, and extreme droughts showed a spatial distribution of low in the center north of southern Guizhou) and high in the borders. (4) There was a strong spatial coupling relationship between karst development intensity, altitude and meteorological-agricultural drought propagation thresholds. The interaction of different factors exhibited a two-factor enhancement and nonlinear enhancement on the propagation threshold. This indicates that synergistic effects of different factors on the propagation threshold were larger than single-factor effects.
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Agricultura , Sequías , Suelo , Meteorología , GeografíaRESUMEN
Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation.
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Contaminantes Atmosféricos , Contaminación del Aire , Contaminantes Atmosféricos/análisis , Dióxido de Nitrógeno/análisis , Meteorología , Tiempo (Meteorología) , Contaminación del Aire/análisis , Monitoreo del Ambiente/métodos , Material Particulado/análisis , China , Conceptos MeteorológicosRESUMEN
Analyzing the influencing factors of fine particulate matter and ozone formation and identifying the coupling relationship between the two are the basis for implementing the synergistic pollutants control. However, the current research on the synergistic relationship between the two still needs to be further explored. Using the Geodetector model, we analyzed the effects of meteorology and emissions on fine particulate matter and ozone concentrations over the "2 + 26" cities at multiple timescales, and also explored the coupling relationship between the two pollutants. Fine particulate matter concentrations showed overall decreasing trends on inter-season and inter-annual scale from 2015 to 2021, whereas ozone concentrations showed overall increasing trends. While ozone concentrations displayed an inverted U-shaped distribution from month to month, fine particulate matter concentrations displayed a U-shaped fluctuation. On inter-annual scale, climatic factors, with planet boundary layer height as the main determinant, have higher effects for both pollutants than human precursors. In summer and autumn, sunshine duration had the most influence on fine particulate matter, while planet boundary layer height was the greatest factor in winter. Fine particulate matter is the leading impacting factor on ozone concentrations in summer, and there were positive associations between them on both annual and seasonal scale. The impact of nitrogen oxides and volatile organic compounds for both pollutants concentrations varied significantly between seasons. The two pollutants concentration were enhanced by the interactions between the various components. On inter-annual scale, interactions between the planet boundary layer height and other factors dominated the concentrations of the two pollutants, whereas in summer, interactions between fine particulate matter and other factors dominated the concentrations of ozone. The study has implications for the treatment of atmospheric pollution in China and other nations and can serve as an important reference for the creation of integrated atmospheric pollution regulation policies over the "2 + 26" cities.
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Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Humanos , Material Particulado/análisis , Ozono/análisis , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Ciudades , Meteorología , Monitoreo del Ambiente , Estaciones del Año , ChinaRESUMEN
Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in the Indo-Gangetic plain region. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10 years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100 years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts.
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Agricultura , Sequías , Humanos , Meteorología , Producción de Cultivos , IndiaRESUMEN
Documenting the rate, magnitude and causes of snow loss is essential to benchmark the pace of climate change and to manage the differential water security risks of snowpack declines1-4. So far, however, observational uncertainties in snow mass5,6 have made the detection and attribution of human-forced snow losses elusive, undermining societal preparedness. Here we show that human-caused warming has caused declines in Northern Hemisphere-scale March snowpack over the 1981-2020 period. Using an ensemble of snowpack reconstructions, we identify robust snow trends in 82 out of 169 major Northern Hemisphere river basins, 31 of which we can confidently attribute to human influence. Most crucially, we show a generalizable and highly nonlinear temperature sensitivity of snowpack, in which snow becomes marginally more sensitive to one degree Celsius of warming as climatological winter temperatures exceed minus eight degrees Celsius. Such nonlinearity explains the lack of widespread snow loss so far and augurs much sharper declines and water security risks in the most populous basins. Together, our results emphasize that human-forced snow losses and their water consequences are attributable-even absent their clear detection in individual snow products-and will accelerate and homogenize with near-term warming, posing risks to water resources in the absence of substantial climate mitigation.
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Actividades Humanas , Nieve , Meteorología , Calentamiento Global/prevención & control , Calentamiento Global/estadística & datos numéricos , Temperatura , Abastecimiento de Agua/estadística & datos numéricosRESUMEN
Based on the physical and geographical conditions, the Baltic Region is categorised as a humid climate zone. This means that, there is usually more precipitation than evaporation throughout the year, suggesting that droughts should not occur frequently in this region. Despite the humid climate in the region, the study focused on assessing the spatio-temporal patterns of droughts. The drought events were analysed across the Baltic Region, including Sweden, Finland, Lithuania, Latvia, and Estonia. This analysis included two drought indices, the Standardized Precipitation Index (SPI) and the Streamflow Drought Index (SDI), for different accumulation periods. Daily data series of precipitation and river discharge were used. The spatial and temporal analyses of selected drought indices were carried out for the Baltic Region. In addition, the decadal distribution of drought classes was analysed to disclose the temporal changes and spatial extent of drought patterns. The Pearson correlation between SPI and SDI was applied to investigate the relationship between meteorological and hydrological droughts. The analysis showed that stations with more short-duration SPI or SDI cases had fewer long-duration cases and vice versa. The number of SDI cases (SDI ≤ -1) increased in the Western Baltic States and some WGSs in Sweden and Finland from 1991 to 2020 compared to 1961-1990. The SPI showed no such tendencies except in Central Estonia and Southern Finland. The 6-month accumulation period played a crucial role in both the meteorological and hydrological drought analyses, as it revealed prolonged and widespread drought events. Furthermore, the 9- and 12-month accumulation periods showed similar trends in terms of drought duration and spatial extent. The highest number of correlation links between different months was found between SPI12-SDI9 and SPI12-SDI12. The results obtained have deepened our understanding of drought patterns and their potential impacts in the Baltic Region.
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Clima , Sequías , Ríos , Meteorología/métodos , Países BálticosRESUMEN
A incidência da dengue, doença viral transmitida pelo mosquito Aedes aegypti, vem crescendo em Porto Alegre ao longo dos anos, com recorde de casos registrados em 2022. Epidemias da doença parecem ocorrer de forma cíclica no município, com registros a cada três anos. Dada a influência de fatores climáticos no ciclo de vida do vetor, este trabalho buscou analisar a influência de determinantes meteorológicos na periodicidade de epidemias de dengue na capital gaúcha entre 2010 e 2022. Análises descritivas foram realizadas para averiguar o padrão dos indicadores climáticos e dos casos de dengue ao longo dos anos, ao passo que análises estatísticas foram feitas para avaliar a correlação entre os fatores climáticos e os casos autóctones registrados entre 2016 e 2022. Os resultados obtidos não apontaram padrões meteorológicos que se repetem a cada três anos e que poderiam explicar a ciclicidade observada. Ainda, não foram constatadas correlações entre temperatura, umidade e pluviosidade com casos autóctones de dengue no município, ao menos em nível quadrimestral. Para além destas análises, constatou-se expressivo aumento de casos em 2022, apesar dos esforços de controle desempenhados pelo poder público, o que aponta a necessidade de maior investimento em educação em saúde para a população. (AU)
The incidence of dengue, a viral disease transmitted by the Aedes aegypti mosquito, has been increasing in Porto Alegre over the years, with a record number of cases reported in 2022. Epidemics of the disease seem to occur cyclically in the city, with reports every three years. Given the influence of climatic factors on the vector's life cycle, this study aimed to analyze the influence of meteorological determinants on the periodicity of dengue epidemics in Porto Alegre between 2010 and 2022. Descriptive analyses were used to investigate the pattern of climatic indicators and dengue cases over the years, while statistical analyses were performed to evaluate the correlation between climatic factors and autochthonous cases registered between 2016 and 2022. The results did not point out meteorological patterns that repeat every three years and could explain the observed cyclicity. Furthermore, no correlations were found between temperature, humidity and rainfall and autochthonous dengue cases in the city, at least on a four-monthly basis. Beyond these analyses, a significant increase in dengue cases was observed in 2022, despite the efforts of the public authorities to control the disease, which highlights the need for greater investment in health education for the population. (AU)
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Clima , Aedes , Dengue , Meteorología , EpidemiasRESUMEN
This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.
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Incendios Forestales , Hawaii , Algoritmos , Aprendizaje Automático , MeteorologíaRESUMEN
Environmental changes such as seasonality, decadal oscillation, and anthropogenic forcing may shape the dynamics of lower trophic-level organisms. In this study, 9-years (2010-2018) of monitoring data on microscopic protists such as diatoms and dinoflagellates, and environmental variables were analyzed to clarify the relationships between plankton and local/synoptic environmental changes. We found that time-series temperature increased in May, whereas it decreased in August and November. Nutrients (e.g., phosphate) decreased in May, remained unchanged in August, and increased in November from 2010 to 2018. The partial pressure of CO2 increased in May, August, and November over time. It is notable that the change in seawater temperature (-0.54 to 0.32 °C per year) and CO2 levels (3.6-5.7 µatm CO2 per year) in the latest decade in the eastern Tsugaru Strait were highly dynamic than the projected anthropogenic climate change. Protist abundance generally increased or stayed unchanged during the examined period. In August and November, when cooling and decreases in pH occurred, diatoms such as Chaetoceros subgenus Hyalochaete spp. and Rhizosoleniaceae temporally increased from 2010 to 2018. During the study period, we found that locally aquacultured scallops elevated soft tissue mass relative to the total weight as diatom abundance increased, and the relative scallop soft tissue mass was positively related to the Pacific Decadal Oscillation index. These results indicate that decadal climatic forcing in the ocean modifies the local physical and chemical environment, which strongly affects phytoplankton dynamics rather than the effect of anthropogenic climate change in the eastern Tsugaru Strait.
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Dióxido de Carbono , Diatomeas , Japón , Meteorología , Agua de Mar/química , AcuiculturaRESUMEN
Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.
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Aprendizaje Profundo , Calidad del Agua , China , Hidrología , Meteorología , OxígenoRESUMEN
At the present stage, collaborative control of particulate matter and ozone pollution has become a modern challenge. The atmospheric boundary layer height (ABLH) is an important meteorological parameter for the sources and sinks of air pollutants. It is generally recognized that the reduction of boundary layer is conducive to the accumulation of pollutants. However, in recent years, some studies have shown that the relationship between ABLH and ozone is not negatively correlated. Here, we analyzed the spatial distribution characteristics of PM2.5 and ozone exceedance in China from 2015 to 2022. The relationships between particulate pollution and ozone pollution and boundary layer meteorology were discussed. The key to coordinated control is to control the PM2.5 concentration in the winter and ozone in summer. Moreover, the two have different responses to meteorological factors, especially to the ABLH. Low temperature and low ABLH are conducive to the deterioration of particulate pollution, but high temperature and high ABLH are conducive to the occurrence and development of ozone pollution. The response of ozone to ABLH is contrary to previous studies in Europe and the United States. Moreover, an abnormal positive correlation was observed for PM2.5 and ABLH in Southwest China, which was mainly due to the impact of biomass combustion in Southeast Asia.
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Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Estados Unidos , Material Particulado/análisis , Ozono/análisis , Contaminación del Aire/análisis , Meteorología , Monitoreo del Ambiente , Contaminantes Atmosféricos/análisis , Polvo , Estaciones del Año , ChinaRESUMEN
This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.
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Cambio Climático , Monitoreo del Ambiente , Monitoreo del Ambiente/métodos , Pakistán , MeteorologíaRESUMEN
Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.