Decrease In Air Quality Research Articles

Analisis Dampak Sumber Daya Alam Pasca Penambangan Batu Andesit di Kabupaten Ponorogo Menggunakan Metode Deskriptif

This research was conducted at an andesite mining industry company in Ponorogo district. This research is based on the mining industry in Indonesia which is very developed and also produces a large impact. The aim of this research is that we can find out the impacts produced by the andesite mining industry. This research uses qualitative methods so that it can be seen before the forest is mined and after the forest is mined. This research produces impact analysis data produced by the andesite mining industry, namely a decrease in air quality, water quality, soil quality and increased noise due to mobilization and mining equipment.

Investigation of emissions from passenger flights Denizli Çardak Airport, Türkiye

AbstractDue to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25min and 3s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Assessment of the Impact of PM2.5 Air Pollution on River Dynamics: A Case Study of Daqing, China.

This study aims to explore the causal relationship between air pollution and river flow in Daqing City, specifically focusing on PM2.5 particle concentration. During comprehensive five-year research, a progressive decrease in air quality was seen, shown by a rise in the yearly average PM2.5 concentration from 13.4 µg/m³ to 18.7 µg/m³. This work utilizes hydrological modeling to reveal a strong negative association between the rise in PM2.5 concentration and the decline in river flow. The decline observed can be attributed to variations in solar radiation and surface temperature, which are clearly impacted by pollution. Ecological investigations showed that the decrease in river flow led to severe ecological impacts, including higher water temperature and alterations in nitrogen levels. This study emphasizes the crucial need to implement thorough environmental management strategies to successfully reduce the detrimental effects of air pollution on river ecosystems. This study enhances our comprehension of the intricate connection between air pollution and river dynamics, on both a regional scale and within the wider context of the southern hemisphere. It highlights the worldwide significance of safeguarding clean air and water resources.

Effects of COVID-19 Policy on Air Quality in Lagos State, Nigeria

The decrease in air quality has been a major concern in the world for decades, and it has continued to worsen with toxic gases accumulating at exponential rates. The atmosphere has been heavily polluted as a result of anthropogenic activities, and these activities were greatly reduced during the COVID-19 pandemic when a lockdown policy was imposed. The study area, Lagos, is the state in Nigeria with the highest population count, making it most susceptible to the spread of the coronavirus and, as such, having the strictest policy regulations. This study seeks to evaluate the effects of the COVID-19 lockdown policy on air quality in Lagos State. The study adopted geographic information systems (GIS) and remote sensing techniques. Goggle Earth Engine (GEE) and Sentinel 5P (S5P) TROPOMI dataset were used to obtain results for carbon monoxide (CO), sulphur dioxide (SO2), aerosols (particulate matter), and nitrogen dioxide (NO2) through JavaScript coding for the year before the lockdown (2019), the year of the lockdown (2020), and the year after the lockdown (2021). Results from the study revealed that the year 2020 had lower concentrations of aerosols and NO2, which increased in 2021 when human and vehicular activities were back to normal. SO2 and CO concen-trations were higher in the year 2020 than in year 2019 and 2021, suggesting that the COVID-19 lockdown was loosely implemented. It is recommended that policymakers invest in green technologies such as solar systems to minimize emissions, enforce strict emission standards for industries and vehicles to limit the release of air pollutants.

Status of vehicle emission standards in the ASEAN region

Vietnam's air quality has declined over the past few decades, especially in urban areas. There is a noticeable decrease in air quality in certain areas due to vehicle emissions. This article aims to assess the automotive emission standards of Asian nations and draw comparisons with Vietnam's emission laws. However, there are significant differences between the present emission ceilings set by different countries. For instance, Korea's pollution regulations do not favor any particular fuel, and light-duty vehicles (LDVs) are not specifically categorized according to weight. Vietnam, Thailand, and Korea have different weight categories and emission limits for cars that run on gasoline and diesel. Consequently, it is imperative to conduct a more thorough investigation into the creation of globally applicable emission standards. This will guarantee uniformity in the public's access to information and policies.

Quantification of sources and potential risks of cadmium, chromium, lead, mercury and arsenic in agricultural soils in a rapidly urbanizing region of southwest China: the case of Chengdu.

Rapid urbanization a major factor affecting heavy metal contamination on suburban agricultural soils. In order to assess the dynamic contamination of heavy metals in soil from agricultural land bordering a rapidly urbanizing area and the transfer of human health risks from contaminants in this process, 186 and 293 soil samples from agricultural land in suburban Chengdu were collected in September 2008 and September 2017, respectively. Several indicators, such as the integrated pollution index (PI) and the potential ecological risk index (RI), were employed for analyzing the heavy metal contamination levels, and the APCS-MLR receptor model were applied for analyzing the heavy metal sources. As a result, mean concentrations for five elements did not exceed the national soil pollution risk screening values in the two periods mentioned above. Nemerow's composite contamination index revealed an increase in soil contamination of arable land after 10 years of urbanization, with 3.75 and 1.02% of light and moderate sample plots, respectively, by 2017. The assessment for potential ecological risk indicated an increased level of eco-risk to high for most of the sample plots. Based on the APCS-MLR model, the origin and contribution to the five elements varied considerably between the two periods mentioned above. Among them, soil Pb changed from "industrial source" to "transportation source," soil Cr changed from "natural source" to "transportation source," and As and Hg changed from "industrial source" to "transportation source." As and Hg were associated with agricultural activities in both periods, and Cd was derived from industrial activities in both periods. The study suggests that inhalation has become a major contributor to non-cancer health risks in urbanization, unlike intake routes in previous periods, and that the increase in cancer risk is mainly due to children's consumption of agricultural products with As residues. The change in the main source of As to "transportation" also indicates a decrease in air quality during urbanization and the development of the transportation industry. This study provides a reference for the governments of rapidly urbanizing cities to formulate relevant highway and agricultural policies to safeguard the health of the people based on the current situation.

Global Warming Towards Human Health: A Literature Review

Global warming is a phenomenon of an increase in the average temperature of the earth's surface due to an increase in the concentration of greenhouse gases in the atmosphere, which not only affects the global ecosystem and climate, but also has a significant impact on human health. These impacts include an increase in heat-related diseases, the spread of infectious diseases, a decrease in air quality, and changes in disease patterns due to climate change. This study aims to identify and analyze the impact of global warming on human health through literature review from various sources such as journal articles, research reports, and health organization documents. The results showed that global warming increases the risk of heat stroke and dehydration, especially in vulnerable groups such as children and the elderly, as well as affecting the geographical distribution of disease vectors such as mosquitoes that can spread malaria and dengue fever. Rising temperatures also worsen air quality by increasing the concentration of surface ozone and airborne particles, which has an impact on the increase in respiratory diseases, as well as changing the seasonal and geographical patterns of diseases, including impacts on people's food security and nutrition. The conclusions of this study underscore the importance of coordinated mitigation and adaptation actions to reduce the health impacts of global warming, including policies to reduce greenhouse gas emissions, improve health systems, and educate the public on preventive measures, with the need for further research for effective and specific adaptation strategies for different affected regions and populations.

Deep Learning Techniques for Air Quality Prediction: A Focus on PM2.5 and Periodicity

The rapid increase in traffic, urbanization, and industrial expansion has all contributed to a decrease in air quality, which has a vital impact on both the long-term feasibility of the environment and the health of humans, particularly in industrialized nations. Numerous studies have explored using machine learning for air quality forecasting to reduce pollution. While shallow machine learning architectures offer less accurate forecasts, deep learning, a recent advancement in computational intelligence, has immense potential in predicting air quality. Deep learning frameworks can identify intricate correlations and patterns in data on air quality, resulting in more accurate and dependable predictions. Several aspects, including climatic conditions, emission sources, and geographical characteristics, may be considered by these models, which can help one better understand and anticipate air pollution levels. This research investigates deep learning applications' periodic changes in air quality. Hybrid deep learning methods utilize optimization, data decomposition, and correlation evaluation between PM2.5 particles and other factors to overcome limitations. This study contrasts various deep learning algorithms for forecasts of air quality and demonstrates that hybrid deep learning is more accurate compared to each model alone at predicting future periods of air quality. It proposes future research directions for the future generation of models. The literature summary provides valuable insights for academics seeking future studies in this field.

Analysis The Effect of Large-Scale Social Restrictions on Air Quality in DKI Jakarta

The Covid-19 pandemic has caused all countries to implement strategies to suppress its spread, one of which is Indonesia, especially DKI Jakarta, which has implemented Large-Scale Social Restrictions (PSBB) since April 10 2020. Apart from being able to suppress the spread of the Covid-19 virus, PSBB is thought to have an impact on the environment, especially air quality in DKI Jakarta. According to research from the BMKG, Jakarta's air quality has improved over the last 5 years with the implementation of the PSBB. Besides analyzing the effect of the PSBB on air quality in DKI Jakarta, this research also aims to help governments in every region of Indonesia that do not have air quality monitoring stations. The method used in this study is to utilize Imagery from Sentinel-5P to measure concentrations of NO2, CO and SO2 gases validated using field data and utilize the NOAA Satellite acquired with Ventusky to analyze the effect of wind on the distribution of air pollution due to the PSBB. The results showed that the ratio of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta decreased respectively to 27.70% ; 10.20% ; 42.06%. This shows an increase in air quality in DKI Jakarta due to the implementation of the PSBB. Comparison of the average concentrations of NO2, CO and SO2 gases in DKI Jakarta during the PSBB and after the PSBB increased slightly respectively to 11.92% ; 1.89% ; 35.84%. This shows that there is a decrease in air quality in DKI Jakarta which was caused after the implementation of the PSBB. Wind also affects the concentration of NO2, CO and SO2 gases. This is evidenced by the results of the correlation where the gas concentration is low when the wind speed is high, and vice versa. It was concluded that during the COVID-19 pandemic the concentrations of NO2, CO and SO2 in DKI Jakarta decreased and slightly increased after the PSBB, and wind could affect the distribution of these gases.

Prediction and Comparison of In-Vehicle CO2 Concentration Based on ARIMA and LSTM Models

An increase in the carbon dioxide (CO2) concentration within a vehicle can lead to a decrease in air quality, resulting in numerous adverse effects on the human body. Therefore, it is very important to know the in-vehicle CO2 concentration level and to accurately predict a concentration change. The purpose of this research is to investigate in-vehicle concentration levels of CO2, comparing the accuracy of an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model in predicting the change in CO2 concentration. We conducted a field test to obtain in-vehicle original concentration data of CO2 while driving, establishing a prediction model of CO2 concentration with ARIMA and LSTM. We selected mean absolute percentage error (MAPE) and root mean squared error (RMSE) as the evaluation indicators. The findings indicate the following: (1) With the vehicle windows closed and recirculation ventilation mode activated, in-vehicle CO2 concentration increases rapidly. During testing, CO2 accumulation rates were measured at 1.43 ppm/s for one occupant and 3.52 ppm/s for three occupants within a 20 min driving period. Average concentrations exceeded 1000 ppm, so it is recommended to improve ventilation promptly while driving. (2) The MAPE of ARIMA and LSTM prediction results are 0.46% and 0.56%, respectively. The RMSE results are 19.62 ppm and 22.76 ppm, respectively. The prediction results demonstrate that both models effectively forecast changes in a vehicle’s interior environment CO2, but the prediction accuracy of ARIMA is better than that of LSTM. The research findings provide theoretical guidance to traffic safety managers in selecting suitable models for predicting in-vehicle CO2 concentrations and establish an effective in-vehicle ventilation warning control system.

The relevancy-analysis of air quality index to Acute Respiratory Infection (ARI) in A landfill site of Medan

The existence of open dumping in Medan City’s final waste processing since 1993 has certainly had a negative impact in the form of a decrease in air quality which has a further impact on public health problems. This study aims to analyse and maps the distribution of PM10 and PM2.5 concentrations in the Terjun Landfill and to examine the relationship between the air quality index and the prevalence of acute respiratory infection (ARI) in the nearby landfill site. The sampling method for this research uses the manual active and is mapped using the GIS application. The results of ambient air concentrations were converted into an air quality index which subsequently was tested in a statistical correlative method to investigate the prevalence of ARI. The results demonstrated the PM2.5 concentrations ranged from 38.94 – 163.1 µ/m3, the PM10 was between 46.84 – 184.2 µ/m3; indicating an excessive level of national ambient threshold. The distribution of the highest PM2.5 and PM10 is in the active zone of the landfill. The prevalence of ARI in the nearby landfill site showed a strong relationship; 0.86 for PM2.5 and 0.62 for PM10. These findings illustrate a poor-quality air would increase the number of inhabitants with ARI.

Sistem Monitoring dan Deteksi Dini Pencemaran Udara Berbasis Internet Of Things (IOT)

The problem of environmental pollution has been a concern for a long time and continues to be an important issue today. One form of pollution that is very detrimental is air pollution, which causes a decrease in air quality and disrupts its normal function. . The ISPU Scale usually consists of several categories that describe the level of air quality, such as the range 1-50 is said to be "Good", the range 51-100 is said to be "Moderate", the range 101-200 is said to be "Unhealthy", the range 201-300 is said to be "Very Unhealthy", and a range of 301+ is said to be "Dangerous". To monitor the level of air pollution, it is necessary to use a tool that can measure air quality accurately. Indoor pollution, as is often the case in office environments, is one of the most serious threats to the health of modern society. A worker can spend up to eight hours a day in the room. The air we breathe everyday comes from the building's air duct system. However, these duct systems not only circulate air, they can also spread contaminants from one room to another. Therefore, it is important to maintain indoor air quality for the comfort and health of workers, because this has a direct impact on their productivity. Based on this, an idea emerged to develop a prototype detector of carbon monoxide gas that is sensitive to indoor air pollution using a microcontroller and web-based. This research begins with analyzing and designing the system, both in terms of hardware and software. After that, the next step involves coding and programming configuration and setup on the Thingspeak web. The last stage is testing tools and systems to ensure that they operate according to the plan that has been set. This system is designed to be able to detect and monitor the level of air pollution, as well as provide a graphical display on Thingspeak. This tool works by using a microcontroller to read the MQ-135 sensor attached to the Ardino Wemos ESP8266. This sensor is used to measure gas levels in a predetermined room. The tool will connect to the Wi-Fi hotspot on the Android device to provide notifications via Thigspeak based on a graph

Prediction method of PM2.5 concentration based on decomposition and integration

With the acceleration of urbanization leading to a general decrease in air quality, accurate PM2.5 concentration prediction is of the utmost practical meaning for the control and prevention of air pollution in the region. Therefore, a new hybrid prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), approximate entropy (ApEn), variational mode decomposition optimized by capuchin search algorithm (CVMD), long short-term memory optimized by pelican optimization algorithm (POA-LSTM) and error correction (EC), named CEEMDAN-ApEn-CVMD-POA-LSTM-EC, is proposed. First, CEEMDAN is used to acquire a limited amount of intrinsic mode functions (IMFs). Second, calculate ApEn value for each IMF component, and divide each IMF component into high-complexity and low-complexity components by the size of ApEn values. Third, variational mode decomposition optimized by capuchin search algorithm (CVMD), named CVMD, is proposed. CVMD is used as a secondary decomposition method to further decompose high-complexity components adaptively into a finite number of IMFs. Fourth, long short-term memory optimized by pelican optimization algorithm, named POA-LSTM, is proposed. POA-LSTM predicts all IMF components, and the results of their predictions are combined to generate the original prediction results. Final, error sequence is decomposed and predicted again by the EC module CVMD-POA-LSTM to obtain prediction results of error sequence, and final prediction results are acquired by combining original prediction results and prediction results of error sequence. The datasets in Beijing, Shanghai, and Xi'an were selected for simulation experiments to demonstrate the superiority of the proposed model. Taking Beijing as an example, RMSE, MAE, MAPE and R2 values are 1.9947, 1.5577, 0.1157 and 0.9947, which are superior to other comparison models and have the best performance.

PM10 promotes an inflammatory cytokine response that may impact SARS-CoV-2 replication in vitro.

In the last decades, a decrease in air quality has been observed, mainly associated with anthropogenic activities. Air pollutants, including particulate matter (PM), have been associated with adverse effects on human health, such as exacerbation of respiratory diseases and infections. High levels of PM in the air have recently been associated with increased morbidity and mortality of COVID-19 in some regions of the world. To evaluate the effect of coarse particulate matter (PM10) on the inflammatory response and viral replication triggered by SARS-CoV-2 using in vitro models. Peripheral blood mononuclear cells (PBMC) from healthy donors were treated with PM10 and subsequently exposed to SARS-CoV-2 (D614G strain, MOI 0.1). The production of pro-inflammatory cytokines and antiviral factors was quantified by qPCR and ELISA. In addition, using the A549 cell line, previously exposed to PM, the viral replication was evaluated by qPCR and plaque assay. SARS-CoV-2 stimulation increased the production of pro-inflammatory cytokines in PBMC, such as IL-1β, IL-6 and IL-8, but not antiviral factors. Likewise, PM10 induced significant production of IL-6 in PBMCs stimulated with SARS-CoV-2 and decreased the expression of OAS and PKR. Additionally, PM10 induces the release of IL-1β in PBMC exposed to SARS-CoV-2 as well as in a co-culture of epithelial cells and PBMCs. Finally, increased viral replication of SARS-CoV-2 was shown in response to PM10. Exposure to coarse particulate matter increases the production of pro-inflammatory cytokines, such as IL-1β and IL-6, and may alter the expression of antiviral factors, which are relevant for the immune response to SARS-CoV-2. These results suggest that pre-exposure to air particulate matter could have a modest role in the higher production of cytokines and viral replication during COVID-19, which eventually could contribute to severe clinical outcomes.

Airborne Microbial Quality Assessment in the Educational Buildings during the COVID-19 Pandemic

Rooms with pollutants have a poor impact of 2-5 times greater than outdoors. The lecture hall had the potential to experience a decrease in air quality. This study was conducted to assess microbiological air quality in the general lecture building I Institut Teknologi Sumatra, Lampung, Indonesia, during the COVID-19 pandemic and its relationship with environmental conditions. This study was conducted using a settling sampling technique to count the number of bacteria and fungi in the air. Samples were collected twice daily for five working days. The results showed that the highest concentrations of bacteria and fungi were found at the wifi corner location, at 36.7–1237.2 CFU/m3 and 225.4–1431.2 CFU/m3, respectively. The highest average concentrations of bacteria and fungi at the wifi corner location were found in the afternoon at 479.1 ± 438.1 CFU/m3 and 800 ± 548.4 CFU/m3, respectively. The three locations did not meet the standards of room suitability for humans with immunodeficiencies based on the ACGIH. The location of the wifi corner did not meet the standards of the Minister of Manpower of the Republic of Indonesia No. 5 of 2018 and the WHO regarding microbial standards in the room. The highest value of the Global Microbial Contamination Index (GIMC/m3) was found in the wifi corner (G4: ≥2000 – ≤4000), which is closely related to population density and ventilation. Environmental factors influence the density of bacteria and fungi at the sampling sites. The relative humidity strongly influenced the concentration of fungi. In addition to relative humidity, bacterial density is also affected by light intensity and the number of people. This indicates that despite restrictions on activities during the COVID-19 pandemic, the room at GKU 1 did not meet the eligibility requirements for students with immune disorders. Doi: 10.28991/CEJ-2023-09-01-09 Full Text: PDF

Sistem Monitoring Kualitas Udara Dalam Ruangan: Measuring Device For Indoor Air Quality (Media-Q)

Today's air pollution is increasingly showing a very alarming condition. Sources of air pollution can come from various activities, including industry, transportation, offices, and housing. These various activities are the biggest contribution of air pollutants that are released into free air. The impact of air pollution is causing a decrease in air quality, which has a negative impact on human health. Based on it, we intend to research and create a prototype of an indoor air detector that is not only portable, but also compact, wearable (can be attached to the body/carried), applicable (easy to use), accessible, and connected to smart phones and websites. The device will monitor the indoor air quality and the results will be sent directly to the connected smart phone and will be stored in the cloud media and can also be accessed via the website. In general, this research will involve 3 connected and integrated devices, namely: air quality measurement device, application on a smart phone and a website and its storage media. This device can be used by everyone, where the data obtained by the device at a certain location, can be a contribution and useful information for others (person-to-person/P2P), and in a system, can work automatically and independently (machine-to-machine/M2M). With the above background, there is a need for a device that is not only affordable but can also be connected to a mobile phone and the internet is an urgent need. The team intends to conduct a study on the device and call it a Measuring Device for Indoor Air Quality or MEDIA-Q.

Investigation of link due to atmospheric turbulence in free space optical communication for optical wireless terrestrial networks

Abstract In recent years, wireless communication systems have exploded in popularity. Optical wireless technology is an excellent alternative to RF wireless, but it has high capacity, data speed, frequency, and license-free spectrum, and it is simple to implement. Optical wireless communication sends data through air using optical beams. Atmospheric turbulence degrades the functioning of free-breathing space photosensitive interaction methods by causing interruptions due to weather conditions like fog, smoke, and different pollutants. In this paper, free-space optical communications using snow and fog attenuations and Numerical Weather Prediction (NWP) method is used to determine the turbulence due to the atmosphere. This method improves the data transmission during unfavorable weather conditions by changing the various parameters like receiver aperture size, the wavelength of the transmitter, etc. Furthermore it shows that decrease in air quality and instability are the double significant causes, that can degrade general usage routine, especially when it’s foggy or raining heavily. The result shows that the aperture size and wavelength should be changed as per the weather condition.

Analysis of Indonesia's Three Major Anthropogenic Pollutants Which Include Various Emission and Fuel Sectors in the 1990-2015 Period

The rapid industrial growth and urbanization in Indonesia over the last two decades have resulted in a significant increase in air pollution, so it has caused a decrease in air quality. An air pollution inventory is needed to determine the level of air quality, emission sector, and the type of pollutant fuel. Air pollutant emission data were obtained from various sources, including Regional Emissions Inventory in Asia (REAS) V3.1, Database Emissions for Global Atmospheric Research (EDGAR) V4.3.2, and Community Emissions Data System (CEDS) V1.0. The data consists of 3 types of emitted pollutants (CO, NOX, and SO2) and two contributing factors (emission and fuel sectors). This study aims to compare data from the emission sources of the three air pollutants, determine the trend of changes in the emission of the three pollutants, and determine the main sectors and fuels that emit the three air pollutants. This research uses the literature study method to collect, visualize, and analyze data. The results showed that between 2005 and 2012, there was a downward trend in emissions in the industrial sector for CO, NOX, and SO2 gases, with the lowest point in August. This is because many industrial sectors have applied the principle of clean energy to reduce air pollution and create clean air. However, the transportation sector showed an increase in CO and NOX emissions and peaked in April and October. Furthermore, the SO2 emissions for the power generation sector fluctuated and peaked in July.

IMPLEMENTASI FUZZY INFERENCE SYSTEM (FIS) METODE TSUKAMOTO UNTUK MONITORING KUALITAS UDARA

The lack of air quality monitoring equipment in areas in Indonesia will have a negative impact on health if there is an unconscious decrease in air quality. This study aims to design an air quality monitoring tool using a fuzzy inference system using the Tsukamoto method to provide information in the form of air quality values. By using 3 different types of pollutant sensors, namely MQ2 to detect smoke, MQ7 to detect carbon monoxide (CO) and Sharp GP2YAUF to detect flying dust levels, the data from the readings of each sensor is processed using the fis tsukamoto method to get the result value and category. air quality which then the results will be displayed on a monitoring website as a medium of information. In the process, a series of tools need to be connected to the network in order to be able to carry out the process of sending sensor data into the database, after the data is stored in the database then the website will perform calculations using the Tsukamoto method automatically to get the results of values and categories of air quality that are read by the sensor which then the results will be displayed on the monitoring website. From this research will produce an air quality monitoring tool using the fuzzy inference system Tsukamoto method as a method to determine the results of values and categories. Keywords: Air Quality Monitoring; FIS Tsukamoto method; Fuzzy Logic