Climatological Stations Research Articles

Seasonal Factors and The Potential of Artificial Insemination (AI) in Dairy Cattle

Dairy cattle are dominant milk producer in Indonesia. Several programs have been carried out to increase the population of dairy cattle and milk production, one of which is Artificial Insemination (AI). Indonesia is a tropical country that has two seasons, namely the rainy season and the dry season. This study was conducted in the KPSBU Lembang working area, West Java, from November 2019 to January 2020. Rainfall data from the BMKG Dramaga Climatology Station and artificial insemination (AI) records of Frisian Holstein dairy cattle from 2017 to 2019 were collected. AI success was assessed using the Service per Conception (S/C) and Conception Rate (CR) parameters, obtained through pregnancy checks two months after insemination. Pregnancy checks were conducted using rectal palpation to confirm conception. Statistical analysis focused on seasonal differences in reproductive performance based on these metrics. Highest S/C value was 2.02 with CR Value 53.27% on rainy season with rainfall 253.67 mm month-1. The analysis showed a strong correlation (α = 0.005) between S/C and CR value (0.89*), very weak correlation between rainfall with S/C (r = -0.05) and CR (r = -0.08). The results of the research show that the rainy season and the dry season are not the main factors affecting the AI in dairy cattle, however they didn’t directly affect the season to increase the productivity of saplings.

Assessing the impact of climate change and reservoir operation on the thermal and ice regime of mountain rivers using the XGBoost model and wavelet analysis

This study presents an analysis of the influence of climatic conditions and the operation of a dam reservoir on the occurrence of ice cover and water temperature in two rivers (natural and transformed by reservoir operations) located in the Carpathian Mountains (central Europe). The analyses are based on data obtained from four hydrological and two climatological stations. The Extreme Gradient Boosting (XGBoost) machine learning model was used to quantitatively separate the effects of climate change from the effects arising from the operation of the dam reservoir. An analysis of the effects of reservoir operation on the phase synchronization between air and river water temperatures based on a continuous wavelet transform was also conducted. The analyses showed that there has been an increase in the average air temperature of the study area in November by 1.2 °C per decade (over the period 1984–2016), accompanied by an increase in winter water temperature of 0.3 °C per decade over the same period. As water and air temperatures associated with the river not influenced by the reservoir increased, there was a simultaneous reduction in the duration of ice cover, reaching nine days per decade. The river influenced by the dam reservoir showed a 1.05 °C increase in winter water temperature from the period 1994–2007 to the period 1981–1994, for which the operation of the reservoir was 65% responsible and climatic conditions were 35% responsible. As a result of the reservoir operation, the synchronization of air and water temperatures was disrupted. Increasing water temperatures resulted in a reduction in the average annual number of days with ice cover (by 27.3 days), for which the operation of the dam reservoir was 77.5% responsible, while climatic conditions were 22.5% responsible.

Kajian Penggerusan (Scouring) pada Dinding Saluran Air Waringin Jaya Kecamatan Bojonggede Kabupaten Bogor

The Waringin Jaya river is a tributary of the Ciliwung river that passes through Waringin Jaya village. Although it is not classified as a river with a large discharge, it is capable of eroding riverbanks and retaining walls, endangering the community. This study aims to analyze the magnitude and depth of local scour that occurs on gabion-type retaining walls, located in the Waringin Jaya River basin, Kec. Bojonggede, Kab. Bogor using the Hec-Ras computer application. The calculation of the discharge plan for the return period of 2 years, 5 years, 10 years, 20 years, 25 years, 50 years, and 100 years using the rational method was carried out based on daily rainfall data at the Citeko Meterological Station and the West Java Climatology Station with a data length of 2007 - 2021. Based on the calculations that have been carried out, the water level on the retaining wall with a flood return period of 2 years, 5 years, 10 years, 20 years, 25 years, 50 years, and 100 years is 0.92 m; 0.99 m; 1.03 m; 1.04 m respectively; 1.06 m; 1.08 m; and 1.10 m, while based on the analysis conducted with Hec-Ras bridge scour, the scour that occurs at each return period flood discharge is 1.24 m; 1.32 m; 1.35 m; 1.39 m; 1.40 m; 1.44 m; and 1.46 m, respectively.

Optimalisasi Komunikasi Penyuluhan Pertanian Dalam Sekolah Lapang Iklim Stasiun Klimatologi Kupang

Climate change has an impact on decreasing agricultural production due to changes in cropping patterns. Farmers have difficulty determining planting times to adapt to climate conditions due to a lack of understanding of climate information. Therefore, agricultural education regarding climate information is conveyed by the government to the public through the Meteorology, Climatology and Geophysics Agency (BMKG). The climate information is then conveyed by BMKG through the climatology station in the Climate Field School (SLI) program. The aim of this research is to determine the optimization of agricultural extension communication in the Kupang climatology station's climate field school. The stages of this research method start from a literature review, data collection in the form of primary data and secondary data, then the data is analyzed. The research results show that optimizing agricultural extension communication in the Kupang Climatology Station SLI is carried out by integrating local wisdom with science. The use of local languages by extension agents is an appropriate extension communication strategy used by SLI. This collaboration creates an understanding of how to read climate change which is used to determine the right planting time.

Bimodalność i długoterminowe trendy wartości ekstremalnych temperatury powietrza

Histograms of air temperature with a bimodal shape are commonly observed in many regions of the world. In this study, we investigate the causes of bimodality in the histograms of daily temperature series (minimum, average, and maximum) for selected climatological stations in Slovakia. Our findings suggest that in the Central European region, the bimodal shape of air temperature histograms is mainly due to the latent heat of freezing, as the surface of snow and ice and the air are thermally coupled. The asymmetry in the air temperature histograms is due to the lower mass heat capacity of ice compared to water and air. The energy-intensive latent heat of conversion of ice to water (and vice versa) results in the more frequent occurrence of ground-layer air temperatures around the freezing point, leading to the formation of the observed local maximum. This has far-reaching implications, such as the calculation of the annual mean air temperature at climatological stations. When calculating the average air temperature, negative temperatures should be given less weight than positive temperatures. Temperatures around 0-6°C should be given higher weight. This may also explain why Arctic regions are experiencing more significant warming than equatorial regions. In the second part of this paper, we analyze the long-term trends of selected temperature indices for the climatological station at Hurbanovo (Slovakia) from 1871 to 2020. Our results indicate statistically significant changes in all temperature indices, with indices related to cold temperatures increasing more significantly than those associated with high temperatures. Finally, study examines theoretical probability distributions to estimate T-year temperatures for temperature indices at the Hurbanovo climate station in Slovakia. The analysis includes three time periods (1901–1960, 1961–2020, and 1991–2020) and reveals significant changes in temperature indices at the Hurbanovo station. The 100-year temperature of TN,min was –35.75°C in 1901–1960, –28.69°C in 1961–2020, and –26.52°C in 1991–2020. The 100-year temperature of TX,max was 39.4°C in 1901–1960 and 39.63°C in 1961–2020. TN,min showed the most significant changes, with the 100-year temperature increasing by up to 7.06°C in 1961–2020 and up to 9.23°C in 1991–2020.

Assessing soil erosion and its drivers in agricultural landscapes: a case study in southern Bahia, Brazil

ABSTRACT Erosion is a worldwide threat to biodiversity conservation and agricultural yield, and it is linked to deforestation. In this study, we aim to assess soil loss in landscapes of Cachoeira River watershed, in southern Bahia, northeastern Brazil. We estimate the role of forests in diminishing soil erosion using the Revised Universal Soil Loss Equation (RUSLE). We compare real and simulated scenarios in which the forest was replaced by agricultural use, also comparing estimates of erosivity factor (R factor) derived from remote sensing and climatological station data. Real and simulated annual soil losses varied from 0 to 15.95 t/year and from 0 to 33.53 t/year along the watershed, respectively. However, only 0.04 and 1.67% of this area is highly and severely exposed to erosion, using data from climatological stations and remote sensing, respectively. Soil loss in the simulated deforested scenario was approximately two times higher than the real annual soil loss, indicating the importance of forest cover to mitigate soil erosion. Moreover, soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations. Conclusively, the practice of agroforestry can be used as an alternative to avoid erosion.

Climatological context of the mid-November 2021 floods in the province of British Columbia, Canada

The Canadian province of British Columbia (BC) is subjected to large-scale, destructive floods. The most dramatic was a mid-November 2021 event when atmospheric rivers (ARs) linked to high-intensity storms caused heavy rainfall in southwestern BC, triggering catastrophic flooding. This study examines 37 floods from 2000 to 2021 using information from over 250 climatological stations and compares events with the mid-November 2021 flood. The dates of the floods showed a bi-modal pattern: a primary season (spring to early summer, 16 floods) and a secondary season (fall to early winter, 21 floods). Five mechanisms controlled these floods: heavy rainfall, rapid snowmelt, severe ice jam, rain-on-snow, and a mixture of snowmelt and ice jam; the mid-November 2021 flood was mainly driven by heavy rainfall. Of the 37 floods, those affected by either heavy rainfall (18 floods) or rain-on-snow (10 floods) were used to derive a relationship between the average daily precipitation amount over the duration of an event and the associated integrated water vapour transport IVT¯. Flood events showed a strong linear relationship between these variables with R2≥0.85, p < 0.05, and values of these parameters were significantly higher for the mid-November 2021 flood than for > 90% of the others, although they were not the highest. The mid-November 2021 flood was also one of the four rainfall-related floods that occurred in the secondary season with IVT‾ > 400 kg m−1 s−1. The frequency of flood events over the last five years of the study period has slightly decreased when considering flood events with unknown insured cost. In contrast, insured costs of these events have increased, suggesting that present-day floods are becoming more impactful and may require changes to flood management strategies to reduce costs.

Dengue hemorrhagic fever prediction in coastal area using geographically weighted regression

Dengue hemorrhagic fever (DHF) is still a health problem globally, including in Indonesia. Geographical and climatic conditions in coastal areas are different from other areas, which may impact differences in environmental risk factors for dengue. This study aims to create a prediction model for the incidence of DHF in coastal areas. The research was conducted in Bantul Regency, Indonesia, involving data from 2015-2019. Dengue incidence data were collected from the health office. Climatic data were from climatology station. Data on altitude and shoreline distance were obtained by geographic information system (GIS) processing. Population density and wide settlement area are obtained from the Bureau of Statistics. The geographically weighted regression (GWR) analysis was carried out using GWR4. The results showed that GWR with a weighting of Fixed Bi-Square Kernel obtained an R2 value of 0.7768, better than the global model (R2 0.5254). It indicates that DHF (Y) in Bantul Regency is 77.68% determined by population density (X1), altitude (X2), settlement area (X3), shoreline distance (X4) and rainfall (X5) and the remaining 22.32% are influenced by other variables which are not investigated. Geographically, the predictor variables explain the DHF incidence with a strong category in the central region, and weak in coastal area.

LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.

Assessing Solar Radiation Forecasting: a WRF-Solar Model Evaluation in Kupang, Indonesia

In response to the challenges faced by solar power plants in Kupang, Indonesia – particularly during the peak of the rainy season – this study aims to enhance the WRF-Solar (Weather Research and Forecasting–Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns – characterized by intense cloud activity during these seasons – pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020–2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47–320 W/m² and MBE values from –41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions.

Analisis Korelasi Kelembaban Udara terhadap Epidemi Demam Berdarah yang Terjadi di Kota Bengkulu

Dengue (Dengue Fever) is a vector bone disease caused by the Aedes Aegepty Mosquito. The aim of this research is to determine the relationship between air humidity levels and the Dengue Fever epidemic that occurred in Bengkulu Municipality. This research is a type of descriptive research with quantitative methods which aims to determine and show the relationship between air humidity levels and cases of Dengue Fever (DHF). This research was conducted in the Municipality of Bengkulu, using quantitative data on air humidity obtained from the Climatology Station (Meteorology, Climatology and Geophysics Agency) Baai Island, Bengkulu City, namely air humidity data from 2009 to 2014 and data on Dengue Fever cases in the Municipality obtained from the Department Bengkulu Province Health from 2009 to 2014 data. Judging from the results, the level of increase in air humidity in Bengkulu Municipality in 2009-2014 ranged from 82% - 86%. It can be seen that the number of dengue fever cases in Bengkulu Municipality in 2009-2014 was around 173 - 472 people. Data processing was carried out by computer using SPSS and Microsoft Excel programs. The results of the analysis using the bivariate test show that air humidity has a relationship between the incidence of Dengue Fever that occurs in the Municipality of Bengkulu, with a value of r=0.408 and p-value=0.007.

A hydro-climatic approach for extreme flood estimation in mountainous catchments

Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

A newly developed model for estimating snow depth in ungauged areas

The presence of snow significantly affects the hydrological cycle and soil moisture globally. Nowadays, with the expansion of science, different satellites can measure snow depth all over the world. By establishing a conditional relationship between mean temperature and the Proportion of Snowfall to Total Precipitation (PSTP), this study proposes a method for snow depth estimation for ungauged stations. The case study of this research is Iran. Most of Iran's climate is arid due to its location in the Middle East, making it one of the countries with the lowest rainfall worldwide. On the other hand, this country enormously depends on water resources. Consequently, an accurate and valid estimate of snow amounts in Iran is essential. To reach this goal, we analyzed daily data from 12 synoptic and climatological stations between 1970 and 2020, including rainfall, snow depth, and mean temperature. For each station, the PSTP at 0.5 °C air temperature intervals was determined and fitted to a double sigmoid model that allows snow depth estimation. The evaluation of the snow depth approximated by the double sigmoid model was done using R2, RRMSE, EF, and Bias statistics against the observational data. R2 values in the Era-5 dataset in all stations are lower than 0.5. Additionally, it should be noted that in certain stations, the bias values surpass 20 mm. Furthermore, in 75 percent of the stations, the RRMSE values exceed 0.6. By utilizing the DSBE model, it is possible to achieve a reduction of 122.4 and 0.7 in Bias and RRMSE values, respectively. The R2 between observed and estimated snow depth was estimated between 0.81 and 1 in ten of the studied stations and 0.65 and 0.8 in the other two stations. Moreover, the findings imply that the proposed model is a suitable technique for estimating snow depth in remote areas lacking snowfall measurement.

Species-Specific Responses of Insectivorous Bats to Weather Conditions in Central Chile.

Insectivorous bats play a crucial role in agroecosystems by providing invaluable pest control services. With the escalating impacts of climate change, a comprehensive understanding of the environmental factors influencing bat activity becomes imperative for their conservation in agricultural landscapes. This study investigates the influence of weather conditions, specifically air temperature and relative humidity, on the timing activity and the relative abundance of five insectivorous bat species in central Chile. Data from automatic bat detectors and climatological stations are utilized for analysis. Our results unveil species-specific behaviors, with Tadarida brasiliensis exhibiting early emergence and extended activity periods compared to other bat species. Histiotus montanus and Lasiurus villosissimus display delayed onsets on more humid evenings, whereas Lasiurus varius and T. brasiliensis initiate activity earlier on colder nights compared to warmer ones. Relative humidity emerges as a key factor influencing relative abundance for all species, with more minutes with bat passes detected on drier nights. These findings suggest that global warming may influence observed bat behaviors, potentially altering foraging patterns and activity levels of these bat species. Moreover, as climate change continues, understanding the long-term impact on bat populations and their adaptive strategies is crucial for effective conservation measures. Further studies exploring these dynamics can provide valuable insights for shaping conservation efforts in the face of evolving environmental challenges.

Profile of Rainfall Patterns and Extreme Air Temperatures 1981-2020 in the Mosunal Region of Indonesia (Case Study: South Sumatra)

Increasing concentrations of greenhouse gases have led to changes in extreme climate events. This study aims to describe the picture of climate change in the South Sumatra Region during the last 40 years, namely from 1981-2020. Rainfall and extreme temperature data were obtained from BMKG data for South Sumatra Province at the Palembang City Climatology Station (SK Palembang City) and the Sultan Mahmud Badaruddin II Meteorological Station (SM SMB II). Maximum temperature data, minimum temperature and rainfall were obtained based on parameter profiles obtained from BMKG data for South Sumatra Province based on the ETCCDMI provisions. Maximum temperature data, minimum temperature and rainfall are processed with RClimDex software so that temperature and extreme rain index data are obtained. These indices are extreme climate indices consisting of TN90p, TX90p, TNn, TNx,TXx, TXx, TMAXmean, TMINmean, RX1day, RX5day, SDII, CDD and CWD. The results of this study indicate that the trend of air temperature will increase significantly in South Sumatra Province, especially the minimum air temperature, then the precipitation pattern will also increase, especially the accumulation of rainfall for 5 consecutive days. There is an increase in the number of dry days and a decrease in the number of wet days, as well as the higher accumulation of daily rainfall but the wet days are decreasing day by day. This is also increasingly having an impact on climate change in South Sumatra Province which is getting hotter day by day and the smoke disaster due to land and peat fires in South Sumatra Province is increasingly worrying.

Pengaruh Perubahan Iklim Terhadap Kedalaman dan Intensitas Hujan

Climate change is a natural phenomenon where there is a very extreme change in the value of climatological elements. The increase in floods and droughts is caused by changes in the characteristics of rain that are far below normal. This study aims to determine the trend of climate change resulting in changes in the characteristics of rain, namely the depth and intensity of rain. Assessment of climate change is seen from trend graphs and the Mann-Kendall statistical test to determine significant changes. To determine the effect of climate change on the characteristics, of rainfall, the calculation is divided into 3 periods, namely period 1 (1994-2003), period 2 (2004-2014), and period 3 (2014-2018). Based on the Mann-Kendall statistical test, climate change also occurred as indicated by a significant Z value, namely the Kediri and Sopak climatological stations 4.64 and 6.26, respectively. The pattern of changes in the daily average rain depth and intensity has occurred from 1994 – 2018 where in period 1 in 1994-2003 there was a normal trend, in period 2 in 2004-2013 there was an increasing trend and in period 3 in 2014-2018, there was an extreme increasing trend

Outgoing Letter Approval System at Class I Climatology Stations in North Sumatra Using the Prototype Method

The mechanism for outgoing correspondence at the North Sumatra Class I Climatology Station is through the approval of the Verifier and Kasklim. The system runs in approving outgoing letters, still going through the traditional process where the Verifier and Kasklim must go through directly in the room. This research aims to develop the intended outgoing letter approval application, to increase the effectiveness and efficiency of outgoing letter approval. This research is development research using the System Development Life Cycle (SDLC) approach and the prototype method which refers to data obtained from interviews with several letter operators at the North Sumatra Class I Climatology Station. Based on testing the system using a black box and evaluating the application by the staff concerned, it was concluded that the application was relevant to needs and could be implemented. This study also provides a basis for further research in optimizing similar systems in the context of climatology stations or other fields that require effective management of outgoing mail approvals.

Climate Village Based On Mitigation And Adaptation With Tri Hita Karana Philosophy In Banyuasri Village

Banyuasri Village is located in Buleleng District, Buleleng Regency, Bali Province, Indonesia, which has an area of 1.45 km2 and is located 15 meters above sea level. The population of Banyuasri Village is 10,704 with the majority earning their living as traders, and civil servants. According to the Secretary of the Banyuasri Village, I Made Sujana Dibya S.E, he explained that rubbish and waste are crucial problems for Banyuasri Village. The increasing risk of natural disasters is felt by the community. The solution that can be proposed by the Ormawa Working and Reasoning Group of the Undiksha Technical and Vocational Faculty at the 2023 Ormawa PPK is through a climate village program based on adaptation and mitigation. The implementation method is that all PPK members are allowed to make program suggestions and take responsibility for the work they hold (participatory). Reference to the significance criteria for conformity with government programs, namely PPN 6/PN6/PP2/KP1/RKP2023 in the RAKER 2023 book, Directorate of Environmental Criminal Law Enforcement and Forestry. From the results obtained, the IKP category achieved 81% significance (SS), the dominance of the most on-target targets was pro-p 4 (67%), pro-p 2 (14%), pro-p 3 (10%), pro- p 1(9%). This is by the observations of the 2023 Bali climatology station's dry season forecast bulletin update. The northern part of the Banyuasri sub-district market is at ZOM 418 (beginning of the second dry season on April 11-20 with a rainfall value of less than 85% with a peak in June.

The effect of human activities on Bali’s Silent Day (Nyepi) in 2022

During Nyepi, all activities are eliminated, including public services, such as closing access to land, sea, and air transportation routes that transit to Bali for one day. In 2022, the BMKG Research and Development Center observed air quality in Nyepi. The measurement aims to determine the relative reduction level of greenhouse gas (GHG) and particulate emissions on Nyepi Day compared to before and after. Greenhouse gases and particulates were measured for seven days, February 28 - March 6, 2022. The measurement locations were over 3 points: Denpasar Regional Office III, Karangasem Geophysics Post, and Jembrana Climatology Station. The results of this field measurement show that daily human activities significantly influence the concentration of pollutant gases and particulates in the air. During Nyepi Day, air concentration improves when all human actions are reduced. This is marked by a decrease in the air’s concentration of pollutant gases (CO, NO2) and dust particles. The reduction in the concentration of pollutant gases and dust particles did vary at each observation location; this was due to the sensitivity of the equipment, the character of each observation location, and the placement of measuring devices. Urban areas filled with community activities have experienced the greatest improvement in air quality compared to suburban areas. Order of measuring instruments close to the location of pollutant sources, even in suburban areas, will decrease the concentration of pollutant gases, similar to that in urban areas. The difference between PM25 data during Nyepi 2022 and the average PM25 for 2020 - 2022 shows a decrease in PM25 concentrations of around 47%, with the highest drop at night.

Precipitation Study in Flood Zones: San Mateo Atenco, State of Mexico, Mexico Case Study

This work is focused on analyzing the causes of flooding in the sub-basin belonging to the municipality of San Mateo Atenco: Almoloya-Otzolotepec, because the municipal territory is constantly threatened by hydrometeorological phenomena, such as hailstorms, frost and torrential rains. The maximum precipitation was analyzed in neighboring climatological stations and the precipitation at different return periods (2 to 1000) was determined through the Gumbel distribution, and its impact according to the Thiessen polygon method. With the information of the 24-hour intensities, the Intensity Duration Frequency (IDF) curves and the hietogram of the study area were plotted using the alternating block method. The above procedure was applied to the precipitation data from the 9 stations that affect the basin. The approach proposed in this work considers all the precipitation variables for the best approximation of the phenomena that may occur in the basin; it is an innovative model because it considers the weighted averages of precipitation for each area of influence and its surroundings; this information is useful for the implementation of flood protection projects, adequate design of drainage and sewage systems, and a rational use of the resource to guarantee better living conditions for the inhabitants. According to the results obtained, there is a slow hydrological response and the areas with the greatest runoff potential (8383&lt;CN&lt;100) are located towards the central part of the basin where there is a significant concentration of bare soil.