Uji Model Aeromod Terhadap Sebaran Particulate Matter 10 µm (PM10) di Sekitar Kawasan PT Semen Padang

Penelitian ini bertujuan untuk menguji model AERMOD dalam memprediksi sebaran PM10 di udara ambien kawasan PT Semen Padang. Lokasi penelitian sebanyak 32 titik berdasarkan 8 arah mata angin dengan jarak 0,5 km, 1 km, 1,5 km dan 2 km dari PT Semen Padang. Pengukuran PM10 menggunakan EPAM 5000 Real Time Particulate Air Monitor dilanjutkan pemetaan dengan software Surfer 11. Waktu pengukuran dibagi menjadi 4 shift, yaitu shift 1 (00.00 – 05.59 WIB), shift 2 (06.00 – 11.59 WIB), shift 3 (12.00 – 17.59 WIB) dan shift 4 (18.00 – 23.59 WIB). Pengambilan data meteorologi (temperatur udara, tekanan udara, kelembapan, kecepatan angin dan arah angin) menggunakan alat Meteorological Station PCE-FWS-20 untuk input data pada AERMET, dilanjutkan prediksi sebaran PM10 menggunakan software AERMOD View 8.9.0. Hasil penelitian menunjukkan konsentrasi PM10 dengan EPAM 5000 berkisar antara 21,0 – 79,0 µg/m3 dengan rata-rata 24 jam sebesar 41,7 µg/m3. Konsentrasi PM10 dengan AERMOD berkisar antara 3,5 sampai 68,0 µg/m3 dengan rata-rata 24 jam sebesar 10,6 µg/m3. Jika dibandingkan dengan baku mutu untuk Peraturan Pemerintah No. 22 Tahun 2021 tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup, lokasi 11 dengan koordinat S 0°56'52.46" dan E 100°27'41.88" pada jarak 1 km kawasan Barat PT Semen Padang tidak memenuhi baku mutu. Model mendekati ideal atau dikatakan sempurna yaitu lokasi arah Timur dan Timur Laut karena elevasi yang lebih tinggi dari sumber emisi dan merupakan arah angin dominan pada siang hari.ABSTRACTThis study aims to test the AERMOD model in predicting the distribution of PM10 in the ambient air of the PT Semen Padang area. The research locations were 32 points based on eight cardinal directions with a radius of 0.5 km, 1 km, 1.5 km, and 2 km from PT Semen Padang. PM10 measurement using EPAM 5000 Real-Time Particulate Air Monitor followed by mapping with Surfer 11 software. The measurement time is divided into four shifts, namely shift 1 (00.00 – 05.59 WIB), shift 2 (06.00 – 11.59 WIB), shift 3 (12.00 – 17.59 WIB), and shift 4 (18.00 – 23.59 WIB). Meteorological data retrieval (air temperature, air pressure, humidity, wind speed and wind direction) using the Meteorological Station PCE-FWS-20 for data input to AERMET, followed by prediction of PM10 distribution using AERMOD View 8.9.0 software. The results showed that the concentration of PM10 with EPAM 5000 ranged from 21.0 – 79.0 g/m3 with a 24-hour average of 41.7 g/m3. The concentration of PM10 with AERMOD ranged from 3.5 - 68.0 g/m3 with a 24-hour average of 10.6 g/m3. When compared with the quality standard for Government Regulation no. 22 of 2021 concerning the Implementation of Environmental Protection and Management, location 11 with coordinates S 0°56'52.46" and E 100°27'41.88" at a distance of 1 km west of PT Semen Padang does not meet the quality standards. The model is close to ideal or is said to be perfect, namely the location of the East and Northeast directions because of the higher elevation of the emission source and the dominant wind direction during the day.

Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks

The use of Machine Learning models is becoming increasingly widespread to assess energy performance of a building. In these models, the accuracy of the results depends largely on outdoor conditions. However, getting these data on-site is not always feasible. This article compares the temperature results obtained for an LSTM neural network model, using four types of meteorological data sources. The first is the monitoring carried out in the building; the second is a meteorological station near the site of the building; the third is a table of meteorological data obtained through a kriging process and the fourth is a dataset obtained using GFS. The results are analyzed using the CV(RSME) and NMBE indices. Based on these indices, in the four series, a CV(RSME) slightly higher than 3% is obtained, while the NMBE is below 1%, so it can be deduced that the sources used are interchangeable.

IMPACT ASSESSMENT OF SUBSTANTIVE HYDRO-CLIMATIC VARIABLES ON 2012 FLOOD EXTENT IN YOLA AND ENVIRONS

Flood is a seasonal phenomenon which is natural in it hazardous implication and occurs when there is relative high flow over the banks of the streams as a combine consequence of high recorded data of hydro-climatic related variables in a given geographical area. Yola North LGA, of Adamawa state had experienced an unprecedented flood in the year 2012 over the past decade which might have been influenced by some hydro-climatic variables and caused devastating effects on lives, properties, farmland and buildings respectively. This study focused on the impact assessment of substantive hydro-climatic variables on 2012 flood event in Yola -North and its environs. The hydro-climatic variables data were obtained from Meteorological station at UBRBDA, Yola for a decade. The amount of rainfall experienced was found to be highest (1085.2mm) in the year 2012 than any other year under consideration (2008-2017) except that of 2016, number of rainy days was highest (81 days) in the year 2012. Similarly, in the month of August in the year 2012 evaporation rate was lowest with about 69 mm than any other month of August in the decade, the annual value of water discharge was highest in the year 2012 over the decade with about 6,340(m3/s), the gauge height was found to be highest with about 7.33 m in the year 2012 and the water level was highest in the month of June, July and September with the corresponding values of 3.37 m, 3.49 m and 6.58 m compared to similar months in the years of the decade respectively. These increased changes in some hydro-climatic data analyzed might be the fundamental natural factor that causes the unique flooding than any other factor in the year 2012 in the study area and over time posed negative impact on agricultural lands. Therefore, the study recommends the urgent need to carry out a comprehensive seasonal hydro-climatic data record simulation analysis and variations with a view of taking them as a recipe and strategies of forecasting and predicting the reoccurrence of such phenomenon. The additional meteorological station should be provided by the government agencies in all agricultural zones of the state for adequate and wide range of hydro-climatic data recording for appropriate prediction of weather indices in future.

A Practical Approach for Estimating the Optimum Tilt Angle of a Photovoltaic Panel for a Long Period—Experimental Recorded Data

In this study, recorded empirical data were applied with a practical approach to investigate the optimal tilt angle of the flat plate collectors facing south for a long period in Tehran, Iran. The data included 20 years of recorded average total radiation on the horizontal plane in Tehran’s meteorological station. Based on the previous studies, the annual optimum tilt angle for Tehran was estimated at 33 degrees annually; however, this estimation does not focus on the energy absorption and effectiveness of changing the tilt angle monthly, seasonally, and bi-annually via measured data. This paper aims to explain this distinction between various radiation receptions with different tilt angle adjustments. This study shows that annual solar cumulative radiation energy gained via a monthly tilt angle can be approximately 7% higher than that achieved with an annual tilt angle setup. Additionally, the seasonal and bi-annual tilt angles have about 6% more annual cumulative radiation absorption than the annual tilt angle setup. Moreover, with consideration of similar monthly received radiation, the results illustrate that the radiation gained with a monthly tilt angle set up was 20% greater in the summer months than an annual tilt angle adjustment.

Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

Distributed intelligent measuring and information system for monitoring coastal environmental parameters

The article presents the results of the study of ways to create distributed measuring networks to control the parameters of the surrounding coastal marine environment and atmosphere. The device, the principle of operation and the measuring capabilities of the meteorological station and the measuring buoy are considered. An example of an intelligent hydrometeorological adaptive system for controlling the parameters of the coastal water area is presented.

Rainfall prediction over Ambon Meteorological Station using Multi-Physics Ensemble WRF-ARW

One of the methods to create good forecast using WRF-ARW modelling is tuning the parameterization. However, this method cannot provide rainfall event probability. Current research result revealed that it was able to simulate and forecast some weather parameters. However, based on the verification results, there were some weather parameters which still had low accuracy. Due to such low accuracy on some weather parameters, the authors were interested in performing post-processing methods in forecasting the weather during extreme weather at Pattimura Ambon Meteorological Station. In this study, we employed multi-physics ensemble prediction system (MEPS) by combining 20 WRF-ARW parameterization schemas, which were processed to obtain the ensemble mean, ensemble spread, and basic probability to get the uncertainty from each weather parameters. Verification process was done by using spreads, skill method and ROC curves. It was discovered that MEPS products have a better skill compared to the forecast control, the correlation value of MEPS products is larger and has the lowest error value. In addition, the result of ROC curves shows that the MEPS has an ability to predict weather condition during cloudy and extreme rain.

Thornthwaite Models for Estimating Potential evapotranspiration in Medan City

Evapotranspiration plays a big role in the hydrology process. Potential Evapotranspiration (PET) always keeps soil moisture available, although an amount of water evaporates through evaporation and transpiration. The Thornthwaite equation uses air temperature and latitude from meteorological observations for estimating PET. Medan City is one of the biggest cities in Indonesia that have a problem with land-use change that affected water balance. This study is to estimate the PET and to learn the water balance in Medan City. The monthly temperature data for the period 2011-2020 is collected from three meteorological stations for estimating PET using the Thornthwaite equation. The highest monthly temperature is in Belawan Maritime Meteorological Station yet the lowest rainfall. The trends of PET depend on the month. The highest PET in Jan.-Apr. and Sep.-Dec. are in Belawan Maritime Meteorological Station, while the highest PET in May-Aug. is in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan. The P-PET has shown negative and positive values. The lowest P-PET is found in Belawan Maritime Meteorological Station in March and the highest P-PET is found in Indonesia Agency for Meteorology Climatology and Geophysics Region I Medan in October.

Numerical Modeling of Climate (on Yakutia example)

Numerical modeling of time series of observations of Yakutsk meteorological station was used for the first time to construct a model of heat and moisture climate variability over the course of a century cycle of solar activity (SA). The lag of precipitation relative to temperature for ¼ of the rhythmic wave was revealed. Consecutive change of climatic phases: cold-wet (CW) warm-wet (WW), cold-dry (CD) and warm-dry (WD) has been established. The nonlinearity of the solar-tropospheric relations at level of intra- and secular oscillations is confirmed. The trends and anomalies of climate changes and permafrost response for the next decades and the current century as a whole are determined.

How the Chaos Theory is Defeated in the Yabu Meteorological Station, Cuba

In this work, 8 weather variables were modeled at the Yabu meteorological station, Cuba, a daily database from the Yabu meteorological station, Cuba, of extreme temperatures, extreme humidity and their average value, precipitation, was used. The force of the wind and the cloudiness corresponding to the period from 1977 to 2021, a linear mathematical model is obtained through the methodology of Regressive Objective Regression (ROR) for each variable that explains their behavior, depending on these 15, 13, 10 and 8 years in advance. It is concluded that these models allow the long-term forecast of the weather, opening a new possibility for the forecast, concluding that the chaos in time can be overcome if this way of predicting is used, the calculation of the mean error regarding the forecast of persistence in temperatures, wind force and cloud cover, while the persistence model is better in humidity, this allows to have valuable information in the long term of the weather in a locality, which results in a better decision making in the different aspects of the economy and society that are impacted by the weather forecast. It is the first time that an ROR model has been applied to the weather forecast processes for a specific day 8, 10, 13 and 15 years in advance.