Minimum Monthly Research Articles

Analysis and Prediction of Health Risk from Heavy Metals in Drinking Water Sources Based on Time Series Model

Health risk analysis can predict and control the risks posed by heavy metals, especially in drinking water, which is a highly sensitive environmental receptors. In order to evaluate heavy metal pollution in drinking water, the monthly average concentrations of As, Cd, Cu, Hg, Ni, and Zn were used to assess the health risk between January 2015 and December 2018 in a drinking water source. Furthermore, Spearman rank correlation coefficient and the ARIMA model were used to analyze temporal variations. The results showed that the monthly average concentrations of heavy metals exceeded the class Ⅲ values as specified by Chinese environmental quality standard for surface water(GB 3838-2002), especially Hg with a minimum monthly average four times more than that set by the standard limits. Overall, the order of carcinogenic risk of As and Cd was decreased; the non-carcinogenic risk of Zn, Cu, Ni, Pb, and Hg was increased. Further, the comprehensive non-carcinogenic risk for adults was lower than 1 throughout the study period except February 2015, when the comprehensive non-carcinogenic risk for children was lower than or close to 1 after October 2017, and the comprehensive carcinogenic risk for children was more than 10-4. Meanwhile, the children's health risks are higher than that for adults, with the main health risk characteristic factors of As, Cd, and Hg. The Spearman rank correlation coefficient were -0.714069, -0.773122, and -0.62234, indicating the significant downward trend from 2015 to 2018. However, the children's comprehensive carcinogenic risk, whose average value was 0.000234 much more than 10-4, had significant upward trend in 2018 with Spearman rank correlation coefficient 0.902098. The ARIMA(3,1,3) model was able to predict the comprehensive carcinogenic risk for children from heavy metals in drinking water, and the result indicated the children comprehensive carcinogenic risk should be monitored to ensure levels between 0.000200 and 0.000302. The study has positive significance for risk warning and environmental management compared to the analysis and prediction of health risk from heavy metals in drinking water sources based on time series models.

Climate Change in the Hydrometeorological Parameters of the Black and Azov Seas (1980\u20132020)

To study the nature of climate change in the hydrometeorological parameters of the Black and Azov Seas—surface air temperature (SAT), sea surface temperature (SST), ice cover, and sea level—under conditions of ongoing global warming, we used reanalyses and remote sensing data, as well as information from known publications of recent years. It was found that against an increase in SAT over the Black–Azov Sea region (+0.053°C/year in 1980–2020) and SST of the Black Sea (+0.052°C/year in 1982–2020), the values of these parameters in the 2000s differ significantly from those in the 1980s–1990s: the maximum average monthly summer and minimum average monthly winter temperatures have increased, as well as the number of mild winters. The average annual SST of the Black Sea, which practically did not exceed 15°C in the 1980s–early 1990s, has exceeded 16°C in most cases since 2010 (maximum 16.71°C in 2018). In the 2010s, the average monthly winter minima, with the exception of the winters of 2011/2012 and 2016/2017, did not fall below 8°С. A consequence of the increase in winter temperatures was a decrease in the ice concentration in the Sea of Azov (the trend of the mean monthly concentration is –1.2%/10 years). From about 2004–2010 in the Black Sea and since 2004 in the Sea of Azov, the tendency towards increase in their levels (on average) has been replaced by a slight decrease, so that the average positive trends for the period 1993–2020 (+0.32 ± 0.16 cm/year in the Black Sea and +0.21 ± 0.05 cm/year in the Sea of Azov) were approximately 2.5 times less than in 1993–2012. The reason for this decrease in levels (on average) in the last 10–15 years was apparently a decrease in the incoming part of the freshwater balance of both seas, which is indirectly confirmed by the observed increase in salinity of their waters.

Rainfall Determines Shallow Soil Seepage in a Piedmont Summer Pasture of Alpine Meadow on the Northeastern Qinghai–Tibetan Plateau

Soil seepage is an important component used for quantifying hydrological processes that remains unclear in high-altitude alpine meadows. Shallow soil seepage was continuously measured using an auto-logged micro-lysimeter (diameter = 30 cm, depth = 30 cm) from July 2018 to June 2019 in a piedmont summer pasture of alpine meadow on the Northeastern Qinghai–Tibetan Plateau. The results showed that all the shallow soil seepage events occurred during the non-frozen period from April to September and that the cumulative amount was 106.8 mm, representing about 1/5 of the annual precipitation. The maximum and minimum monthly soil seepage were 30.7 mm in September and 1.0 mm in April, respectively. The boosted regression trees (BRT) model’s area under the curve averaged 0.92 and revealed that the daily half-hour rainfall frequency, volumetric soil water content, and air temperature played significant roles in the daily soil seepage probability, with the cumulative relative contribution of 68%. The stepwise linear regression analysis showed that the rainfall amount accounted for 59% of the variation in the daily amount of soil seepage. The monthly soil seepage was found to be significantly correlated with the monthly rainfall frequency (r = 0.86, p = 0.005). Our results highlighted that rainfall, including its amount and frequency, was the key determinant of the probability and amount of shallow soil seepage in the piedmont summer pasture of alpine meadows. These findings will be helpful for improving predictions of the water budgets of piedmont alpine meadows.

Ground-based high-resolution remote sensing of sulphur hexafluoride (SF6) over Hefei, China: characterization, optical misalignment, influence, and variability.

It is a challenge to retrieve atmospheric sulphur hexafluoride (SF6) with high resolution solar spectra because it has only one single retrieval micro window and has interfered with many factors in the retrieval. Optical misalignment is one of the key factors that affect the accuracy of SF6 retrieval. In this study, we first present a long term time series of the SF6 total column over Hefei, China, between January 2017 and December 2020, retrieved by mid-infrared (MIR) solar spectra recorded by ground-based high-resolution Fourier transform infrared spectroscopy (FTIR). The sensitivities of the total column, root mean square of fitting residual (RMS), total error budgets, degrees of freedom for signal (DOFs), and vertical mixing ratio (VMR) profile with respect to different levels of optical misalignment for SF6 retrieval were assessed. The SF6 total column is sensitive to optical misalignment. In order to avoid inconsistencies in the total column due to optical misalignment, we use the true instrumental line shape (ILS) derived from regular low-pressure HBr cell measurements to retrieve the time series of SF6. The total column of SF6 over Hefei presents strong seasonal dependent features. The maximum monthly average value of (3.57 ± 0.21) × 1014 molecules*cm-2 in summer is (7.60 ± 3.50) × 1013 molecules*cm-2 (21.29 ± 9.80) % higher than the minimum monthly average value of (2.81 ± 0.14) × 1014 molecules*cm-2 in winter. The annual average SF6 total columns in 2017-2020 are (3.02 ± 0.17), (3.50 ± 0.18), (3.25 ± 0.18), and (3.08 ± 0.16) × 1014 molecules*cm-2, respectively, which are close to each other. It indicates that SF6 total column over Hefei is stable in the past four years. Our study can improve the current understanding for ground-based high-resolution remote sensing of SF6 and also contribute to generate new reliable remote sensing data in this sparsely monitored region for investigations of climate change, global warming, and air pollution.

O BENEFÍCIO DE PRESTAÇÃO CONTINUADA E O CRITÉRIO DA RENDA PER CAPITA.

The objective of this work is to address the topic of the Continuous Service Benefit, from the perspective of miserability, providing a debate on the aspecto of per capita income, in conditions of inclusion of the ‘’hypo-sufficient’’ citizens to the assistance provided by the State. The guarantee of a minimum monthly wage for the disabled or elderly person who proves that he doesn’t have the means to provide for his own maintenance or to have it provided by his Family, is a way of protecting the class of the population of 65 years or more or those with limitations , in order to provide them with the minimum existential according to human dignity.

PHENOLOGY, REPRODUCTIVE BIOLOGY AND SPATIAL DISTRIBUTION OF Chresta scapigera (LESS.) Gardner (ASTERACEAE)

Chrestas capigera (Less.) Gardner is an important medicinal herb which, however, has been poorly studied for its biology and ecology. This study aimed to investigate its phenology, floral biology, reproductive biology (self-pollination tests), spatial distribution and correlations between phenophases and climatic data in two sites (Cerrado stricto sensu and Campo rupestre) in southern Minas Gerais, Brazil. From August of 2012 to August of 2013, we monitored phenophase occurrence for 70 individuals: emission of new leaves, flowering, production of immature fruits, and mature fruits. Floral anthesis occurred during daytime and remained all day until fruit formation. Peak leaf emergence was observed in April, correlating with minimum monthly temperature and mean monthly precipitation. Flowering and green fruit peaked in May and June, respectively, and correlated negatively with all climatic variables. Mature fruits peaked in June, but did not correlate significantly with any of the climatic variables. However, no difference was found between the two sites regarding the timing of phenophases. The spatial distribution pattern of individuals within sites was random. The self-pollination tests showed that the individuals pollinated and fertilized themselves. Our findings allow us to conclude that the phenology of C. scapigera has pronounced phenological seasonality with reproductive peak activities in the drier and colder season, which is congruent with the self-pollination and anemochoric dispersion strategy.

Analysis of dependent data aggregated into intervals

Many series of data record individual observations as intervals, such as stock market values with daily high-low values, or minimum and maximum monthly temperatures, recorded over time. With the advent of massively large data sets from contemporary computers, it is frequently the case that observations are aggregated into (meaningful) classes of observations, so that resulting data are perforce intervals (or histograms, or other forms of so-called symbolic data). What is known is that taking the average stock market price, or average temperature results in a loss of information. Therefore, this article looks at autocovariance/autocorrelation functions for interval-valued autoregressive series models. Maximum likelihood estimators are derived by exploiting the ideas of composite likelihood and the pairwise likelihood of Lindsay (1988) and Davis and Yau (2011). Different internal distributions of the intervals themselves are also studied. Asymptotic properties of these estimators are obtained. A simulation study shows that the new estimators perform considerably better than those obtained from the set-valued estimators of Wang et al. (2016).

Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder

BackgroundBipolar disorder is associated with circadian disruption and a high risk of suicidal behavior. In a previous exploratory study of patients with bipolar I disorder, we found that a history of suicide attempts was associated with differences between winter and summer levels of solar insolation. The purpose of this study was to confirm this finding using international data from 42% more collection sites and 25% more countries.MethodsData analyzed were from 71 prior and new collection sites in 40 countries at a wide range of latitudes. The analysis included 4876 patients with bipolar I disorder, 45% more data than previously analyzed. Of the patients, 1496 (30.7%) had a history of suicide attempt. Solar insolation data, the amount of the sun’s electromagnetic energy striking the surface of the earth, was obtained for each onset location (479 locations in 64 countries).ResultsThis analysis confirmed the results of the exploratory study with the same best model and slightly better statistical significance. There was a significant inverse association between a history of suicide attempts and the ratio of mean winter insolation to mean summer insolation (mean winter insolation/mean summer insolation). This ratio is largest near the equator which has little change in solar insolation over the year, and smallest near the poles where the winter insolation is very small compared to the summer insolation. Other variables in the model associated with an increased risk of suicide attempts were a history of alcohol or substance abuse, female gender, and younger birth cohort. The winter/summer insolation ratio was also replaced with the ratio of minimum mean monthly insolation to the maximum mean monthly insolation to accommodate insolation patterns in the tropics, and nearly identical results were found. All estimated coefficients were significant at p < 0.01.ConclusionA large change in solar insolation, both between winter and summer and between the minimum and maximum monthly values, may increase the risk of suicide attempts in bipolar I disorder. With frequent circadian rhythm dysfunction and suicidal behavior in bipolar disorder, greater understanding of the optimal roles of daylight and electric lighting in circadian entrainment is needed.

The reduction in C&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;H&amp;lt;sub&amp;gt;6&amp;lt;/sub&amp;gt; from 2015 to 2020 over Hefei, eastern China, points to air quality improvement in China

Abstract. Ethane (C2H6) is an important greenhouse gas and plays a significant role in tropospheric chemistry and climate change. This study first presents and then quantifies the variability, sources, and transport of C2H6 over densely populated and highly industrialized eastern China using ground-based high-resolution Fourier transform infrared (FTIR) remote sensing along with atmospheric modeling techniques. We obtained a retrieval error of 6.21 ± 1.2 (1σ)% and degrees of freedom (DOFS) of 1.47 ± 0.2 (1σ) in the retrieval of C2H6 tropospheric column-averaged dry-air mole fraction (troDMF) over Hefei, eastern China (32∘ N, 117∘ E; 30 ma.s.l.). The observed C2H6 troDMF reached a minimum monthly mean value of 0.36 ± 0.26 ppbv in July and a maximum monthly mean value of 1.76 ± 0.35 ppbv in December, and showed a negative change rate of −2.60 ± 1.34 % yr−1 from 2015 to 2020. The dependencies of C2H6 troDMF on meteorological and emission factors were analyzed using generalized additive models (GAMs). Generally, both meteorological and emission factors have positive influences on C2H6 troDMF in the cold season (December–January–February/March–April–May, DJF/MAM) and negative influences on C2H6 troDMF in the warm season (June–July–August/September–October–November, JJA/SON). GEOS-Chem chemical model simulation captured the observed C2H6 troDMF variability and was, thus, used for source attribution. GEOS-Chem model sensitivity simulations concluded that the anthropogenic emissions (fossil fuel plus biofuel emissions) and the natural emissions (biomass burning plus biogenic emissions) accounted for 48.1 % and 39.7 % of C2H6 troDMF variability over Hefei, respectively. The observed C2H6 troDMF variability mainly results from the emissions within China (74.1 %), where central, eastern, and northern China dominated the contribution (57.6 %). Seasonal variability in C2H6 transport inflow and outflow over the observation site is largely related to the midlatitude westerlies and the Asian monsoon system. Reduction in C2H6 abundance from 2015 to 2020 mainly results from the decrease in local and transported C2H6 emissions, which points to air quality improvement in China in recent years.

The Forecasting Model with Climate Variables of the Re-emerging Disease Rate in Elderly Patients

This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely similar to the model without the climate variables in the combined analysis although such climate variables had relationship with the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin. Therefore, the appropriate model should be the SARIMA model because it is easier for analysis but with higher forecasting accuracy than the SARIMAX model.

The Determination of Visual Inspection in Women of Childbearing Age

Cervical cancer is the most common disease affecting women around the world. Early detection of cervical cancer is needed by using Visual Inspection with Acetic Acid (VIA). This method is relatively easy to do, and the examination costs are also quite affordable, making it more beneficial for women. This study was to determine the determinants of the VIA Test results. It is quantitative research with a descriptive approach presented in the form of a frequency distribution table. There are 100 samples of women of childbearing age acquired by purposive sampling at Patumbak District, Deli Serdang. From the samples, some respondents receive positive IVA test result as follows: 14% aged 36-35 years old, 14% Protestant, 19% married, 9% high school graduates, 10% housewife, 15% with minimum monthly income, 12% with 3-5 children, 17% has multigravida parity, 14% married at the age of 17-25 years old, 12% first-time mother at the age of 17-25 years old, and 14% with 5+ years using IUD contraception. The study was based on the negative VIA test result but with detected pre-cancerous lesions. Further research with different variables is necessary to acquire more information regarding cervicitis incidence causes, thus, reducing cervical cancer numbers.

Temporal variabilities and correlation of runoff in the Ural River

The article deals with the study of intra-annual discharge distribution of the Ural River basin runoff. Norms, coefficients of variation, and average standard deviations of monthly, seasonal and annual runoff, as well as maximum and minimum flow and the tendencies of its changes, were determined for rivers in the area. The average and maximum water discharge spring high water and the ratio of maximum and minimum monthly and the annual runoff were calculated. Based on the wavelet transform method applied to the river flow data of the area under study, a cyclical pattern of water availability was identified. Moreover, the closeness of the correlation between average monthly expenditures and annual average water consumption, as well as the average monthly water costs was revealed. The statistical significance of the detected correlations was defined.

Midwives providing maternal health services to poor women in the private sector: is it a financially feasible model?

Governments in many low- and middle-income countries have increasingly turned to the private sector to address the gap in skilled birth attendance in rural areas. They draw on limited, but emerging evidence that the poor also seek private healthcare services. A question not addressed in this policy and strategy is: Can poor women pay the fees required for private-sector maternity care providers to financially sustain their practices? This article examined the financial viability of private-sector midwifery practices established to provide skilled birth services to Afghan refugee women in Baluchistan, Pakistan. An international non-governmental organization established 45 midwifery practices as part of a poverty alleviation project aimed at providing market-based solutions for female poverty. A retrospective micro-cost analysis was conducted on a sample of 11 practices. In-depth interviews were conducted with 33 stakeholders to explore the midwives' experiences of operating private practices, and the facilitators and barriers they experienced. The single midwife-practices saw a mean of 8.7 ANC patients (range 1-19), attended 2.9 births (range 0-10) and provided care to 1.6 postnatal patients (range 0-7). The average net income of the 11 practices in May 2014 was US$81, but the median was just US$12. To contextualize these incomes, the midwives earned, on average, 25% of Pakistan's minimum monthly living wage. The financial analysis showed only 3 out of 11 sampled practices could be considered financially viable. The qualitative data revealed that even in practices with reasonable client volumes, the patients' inability to pay was the critical factor in the midwife practices' low net incomes. The research provides empirical evidence of a potential pitfall of private funding models in resource-poor settings where providers rely on impoverished clients to pay user-fees. Such financial models essentially shift the government's responsibility to provide safe childbirth services onto providers who can least afford to offer such care.

Climate drivers of Arctic tundra variability and change using an indicators framework

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Mapping the drivers of formaldehyde (HCHO) variability from 2015 to 2019 over eastern China: insights from Fourier transform infrared observation and GEOS-Chem model simulation

Abstract. The major air pollutant emissions have decreased, and the overall air quality has substantially improved across China in recent years as a consequence of active clean air policies for mitigating severe air pollution problems. As key precursors of formaldehyde (HCHO) and ozone (O3), the volatile organic compounds (VOCs) in China are still increasing due to the lack of mitigation measures for VOCs. In this study, we investigated the drivers of HCHO variability from 2015 to 2019 over Hefei, eastern China, by using ground-based high-resolution Fourier transform infrared (FTIR) spectroscopy and GEOS-Chem model simulation. Seasonal and interannual variabilities of HCHO over Hefei were analyzed and hydroxyl (OH) radical production rates from HCHO photolysis were evaluated. The relative contributions of emitted and photochemical sources to the observed HCHO were analyzed by using ground-level carbon monoxide (CO) and Ox (O3 + nitrogen oxide (NO2)) as tracers for emitted and photochemical HCHO, respectively. Contributions of emission sources from various categories and geographical regions to the observed HCHO summertime enhancements were determined by using a series of GEOS-Chem sensitivity simulations. The column-averaged dry air mole fractions of HCHO (XHCHO) reached a maximum monthly mean value of 1.1 ± 0.27 ppbv in July and a minimum monthly mean value of 0.4 ± 0.11 ppbv in January. The XHCHO time series from 2015 to 2019 over Hefei showed a positive change rate of 2.38 ± 0.71 % per year. The photochemical HCHO is the dominant source of atmospheric HCHO over Hefei for most of the year (68.1 %). In the studied years, the HCHO photolysis was an important source of OH radicals over Hefei during all sunlight hours of both summer and winter days. The oxidations of both methane (CH4) and nonmethane VOCs (NMVOCs) dominate the HCHO production over Hefei and constitute the main driver of its summertime enhancements. The NMVOC-related HCHO summertime enhancements were dominated by the emissions within eastern China. The observed increasing change rate of HCHO from 2015 to 2019 over Hefei was attributed to the increase in photochemical HCHO resulting from increasing change rates of both CH4 and NMVOC oxidations, which overwhelmed the decrease in emitted HCHO. This study provides a valuable evaluation of recent VOC emissions and regional photochemical capacity in China. In addition, understanding the sources of HCHO is a necessary step for tackling air pollution in eastern China and mitigating the emissions of pollutants.

Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM

The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted.

Aplicação do modelo MGB-IPH na bacia hidrográfica do Rio Pardo em diferentes cenários de uso e ocupação do solo

As alterações causadas pela ação do homem ao meio ambiente podem influenciar a disponibilidade de água em bacias hidrográficas, e mensurar os impactos causados por essa ação é imprescindível para a tomada de decisões por órgãos gestores. Dessa forma, o objetivo deste estudo foi realizar a calibração e validação do modelo MGB-IPH para simular o comportamento da vazão na bacia hidrográfica do rio Pardo em diferentes cenários de uso e ocupação do solo. Para isso, foram usadas informações de uso e ocupação do solo que revelaram que a bacia hidrográfica em estudo teve uma redução em sua área de floresta e aumento das áreas dedicadas às atividades agropecuárias, entre os anos de 2001 e 2016. Com essas informações acerca da bacia e com os dados climáticos e os tipos de solos disponíveis, foram realizadas a calibração e a validação do modelo, que teve seus desempenhos verificados. As mudanças nas vazões mensais médias, totais, máximas e mínimas simuladas pelos diferentes cenários foram comparadas por meio de teste de média para amostras dependentes e avaliação das vazões anuais máximas, médias e mínimas dos valores testados. O MGB-IPH foi validado de forma satisfatória para a estação 53880000 da Agência Nacional de Águas, tendo problemas de subestimativa de picos de vazões máximas, porém conseguindo representar adequadamente os dados observados em termos da sazonalidade das vazões, da diferenciação de anos mais úmidos e anos mais secos, e dos picos de cheias de média e pequena intensidade. O modelo foi sensível ao identificar o impacto das mudanças do uso e ocupação do solo nas vazões simuladas para a bacia hidrográfica do rio Pardo, com o cenário de 2016 apresentando maiores valores de vazões mensais médias, totais, máximas e mínimas que as encontradas no cenário de 2001.

A COMPARISION USING STATISTICAL AND MACHINE LEARNING METHODS FOR STREAMFLOW TIME SERIES

This study was carried out in the Sibinacocha lake watershed in the Peruvian Andes. In this region the long-term meteorological data are scarce and there are few studies of flow forecasts. Based on this evidence, in this study we present the monthly flow simulation, using statistical models and data-oriented model, with the purpose of evaluating the performance of these methodologies. The results of the comparative statistical analyses indicated that the data-oriented models, specifically the Recurrent Neural Networks, provided great improvements over the other models applied, specifically the ability to capture the minimum and maximum monthly flow, resulting in excellent statistical values (R2=0.85, d=0.96), thus suggesting this methodology as a possible application for flow forecasts.

Remote Sensing of Atmospheric Hydrogen Fluoride (HF) over Hefei, China with Ground-Based High-Resolution Fourier Transform Infrared (FTIR) Spectrometry

Remote sensing of atmospheric hydrogen fluoride (HF) is challenging because it has weak absorption signatures in the atmosphere and is surrounded by strong absorption lines from interfering gases. In this study, we first present a multi-year time series of HF total columns over Hefei, China by using high-resolution ground-based Fourier transform infrared (FTIR) spectrometry. Both near-infrared (NIR) and mid-infrared (MIR) solar spectra suites, which are recorded following the requirements of Total Carbon Column Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC), respectively, are used to retrieve total column of HF (THF) and column-averaged dry-air mole fractions of HF (XHF). The NIR and MIR observations are generally in good agreement with a correlation coefficient (R) of 0.87, but the NIR observations are found to be (6.90 ± 1.07 (1σ)) pptv, which is lower than the MIR observations. By correcting this bias, the combination of NIR and MIR observations discloses that the XHF over Hefei showed a maximum monthly mean value of (64.05 ± 3.93) pptv in March and a minimum monthly mean value of (45.15 ± 2.93) pptv in September. The observed XHF time series from 2015 to 2020 showed a negative trend of (−0.38 ± 0.22) % per year. The variability of XHF is inversely correlated with the tropopause height, indicating that the variability of tropopause height is a key factor that drives the seasonal cycle of HF in the stratosphere. This study can enhance the understanding of ground-based high-resolution remote sensing techniques for atmospheric HF and its evolution in the stratosphere and contribute to forming new reliable remote sensing data for research on climate change.

Potential distribution of Guiana dolphin (Sotalia guianensis): a coastal-estuarine and tropical habitat specialist

Abstract Ecological niche models (ENMs) predict where species can occur in accordance with environmental factors. Suitability maps are generated through models to identify habitats more or less adapted to the species. Published works on the distribution and habitat use of Guiana dolphin, Sotalia guianensis, are limited to fine spatial scales. Here, we aimed to predict the potential geographical distribution of Guiana dolphins through ENMs and generate a map of suitable habitats for the species. Data were collected between 1997 and 2015 in Brazil, French Guiana, and Colombia. The environmental data were obtained from MARSPEC database with a cell resolution of 10 × 10 km. For modeling, 99 of the 859 initial occurrence points of the species were considered after rarefaction. Seven environmental variables were selected through factorial analysis: bathymetry, distance to shore, bathymetric slope, sea surface salinity (minimum monthly and annual range), and sea surface temperature (mean annual and annual range). Results from five distinct algorithms were assembled to generate the distribution model. Our findings show potential areas in shallow platforms of the continental margin of South and Central America, including regions where the species has never been reported, such as the Pacific Ocean, the Gulf of California, the Gulf of Mexico, and the oceanic islands in the Caribbean Sea. The absence of Guiana dolphins in these regions may be due to geographical (linking of North and South America), physical (water temperature), and biological (competition, limited ability to dispersal) limiting factors. The models suggest that the presence of other species of coastal dolphin may be an important limiting factor for the Guiana dolphin at both extremes of its distribution. The Guiana dolphin is habitat specialist with a clinal potential geographic distribution concentrated in tropical and subtropical shallow and coastal waters of the continental shelf of the western Atlantic Ocean. This more restricted distribution than reported by IUCN and other studies suggests a cautionary approach to its conservation status due to limited dispersal abilities and high overlap with human activities.