Decrease In Air Quality Research Articles

Monitoring and calculation of the air pollution standard index (APSI) of Bandung Regency

Abstract Air pollution is directly proportional to the rate of economic growth. With the growing economic life, more people will use high-tech materials, which can cause air pollution. The main problem of this pollution is decreasing air quality. This decrease in air quality is caused by many things, including excessive environmental exploitations, industrial activity, energy use, and vehicles exhaust emissions, all of which trigger pollution problems. Bandung Regency is surrounded by many industries, i.e., textile, garment, metal, printing, metal, food, furniture, and plastic. Thus, monitoring air pollution is imperative. The air parameters measured were Carbon monoxide and Nitrogen dioxide. The measurement duration is every 14 days, from June to July 2021. The sample location is divided into four regions, Margahayu, Kopo Katapang, Baleendah, and Dayeuh Kolot. The method used was the MCU 6814 sensor which has been calibrated by IQAir. Carbon monoxide measurements were taken every 8 hours, while nitrogen dioxide was measured every hour of the day. The average measurement for the four areas in the morning and evening was relatively good, 3-5 g/m3 for CO in the morning and 10-14 g/m3 at peak activity during the day. These results indicated the unhealthy air category.

The Prevalence Overview of ARI Sufferers in the Mining Environment at Cinta Jaya Inc, Molawe District

Introduction: Mining activities also cause traffic disturbances due to the back and forth of operational vehicles and transporters going and out of the special terminal area,causing a decrease in air quality and noise which can increase the prevalence of Ari based on data from molawe health center with a population 0f 5291,30 ARI patient, cases in 2019 and there were 37 cases in 2020 and 42 casess in 2021 this can 2021.this affect the health status of the community in the mining envirotment of the molawe sub-district
 Methods: The method used in this study is descriptive research method. The descriptive method was chosen because the study carried out aims to describe the data naturally. The data used are secondary data and documents from the Health Office of Southeast Sulawesi Province, Limbo Health Center, Molawe District, 2019 - 20121. North Konawe Regency, Southeast Sulawesi Province.
 Results: It was concluded that there was a relationship between age and gender with the incidence of ARI in infants Smoking Habits From 42 respondents, the results showed that most of the respondents were exposed to cigarette smoke, namely 34 respondents or 81.0% and the remaining only 8 respondents (19%) who had no exposure to cigarette smoke. Based on these data, it can be concluded that most children under five are exposed to cigarette smoke.
 Conclusion: The factors that influence the increasing prevalence of ARI are redidental density,smoking/behavior and mining activities that are very at risk for suffering from ARI disease

ANALISA DESKRIPTIF PENGELOMPOKAN DATA KONSENTRASI PM2,5 BERDASARKAN HARI PADA TITIK PEMANTAUAN BUNDARAN HI JAKARTA UNTUK DATA FEBRUARI-OKTOBER 2021

The very high flow of vehicles passing through Bundaran HI has resulted in a decrease in air quality in this area. PM2,5 is one of the pollutant substances produced from vehicle fumes that can be harmful to health. The number of vehicles that pass through this area depends on the presence of office and business activities. The purpose of this study was to determine the pattern of distribution of PM2.5 concentration data by day and how much data did not exceed the quality standard. The research was conducted by descriptive method. The results showed that the pattern of distribution of PM2.5 data which was below the quality standard occurred in February to mid-April 2021 and early June to early July. This is due to the PSBB and PPKM that apply in the city of Jakarta. When the regulation was revoked, the concentration of PM2.5 was more dominant above the quality standard. If the data is grouped by day, the percentage of PM2.5 concentration data that does not pass the quality standard is Monday 43.59%, Tuesday 35.89%, Wednesday 38.46%, Thursday 41.02%, Friday 41.03%, Saturday 56 .41%, Sunday 41.02%..

Structure Analysis of the Sea Breeze Based on Doppler Lidar and Its Impact on Pollutants

The Doppler lidar system can accurately obtain wind profiles with high spatiotemporal resolution, which plays an increasingly important role in the research of atmospheric boundary layers and sea–land breeze. In September 2019, Doppler lidars were used to carry out observation experiments of the atmospheric wind field and pollutants in Shenzhen. Weather Research and Forecasting showed that the topography of Hongkong affected the sea breeze to produce the circumfluence flow at low altitudes. Two sea breezes from the Pearl River Estuary and the northeast of Hong Kong arrived at the observation site in succession, changing the wind direction from northeast to southeast. Based on the wind profiles, the structural and turbulent characteristics of the sea breeze were analyzed. The sea breeze front was accurately captured by the algorithm based on fuzzy logic, and its arrival time was 17:30 on 25 September. The boundary between the sea breeze and the return flow was separated by the edge enhancement algorithm. From this, the height of the sea breeze head (about 1100 m) and the thickness of the sea breeze layer (about 700 m) can be obtained. The fluctuated height of the boundary and the spiral airflow nearby revealed the Kelvin–Helmholtz instability. The influence of the Kelvin–Helmholtz instability could be delivered to the near-surface, which was verified by the spatiotemporal change of the horizontal wind speed and momentum flux. The intensity of the turbulence under the control of the sea breeze was significantly lower than that under the land breeze. The turbulent intensity was almost 0.1, and the dissipation rate was between 10−4 and 10−2 m2·s−3 under the land breeze. The turbulent intensity was below 0.05, and the dissipation rate was between 10−5 and 10−3 m2·s−3 under the sea breeze. The turbulent parameters showed peaks and large gradients at the boundary and the sea breeze front. The peak value of the turbulent intensity was around 0.3, and the dissipation rate was around 0.1 m2·s−3. The round-trip effect of sea–land breeze caused circulate pollutants. The recirculation factor was maintained at 0.5–0.6 at heights where the sea and land breeze alternately controlled (below 600 m), as well as increasing with a decreasing duration of the sea breeze. The factor exceeded 0.9 under the control of the high-altitude breeze (above 750 m). The convergence and rise of the airflow at the front led to collect pollutants, causing a sharp decrease in air quality when the sea breeze front passed.

CLIMATE CHANGE AND ITS IMPACT ON THE HUMAN RIGHTS: NORTH-EAST INDIA IN CONTEXT

The impact of climate change upon the basic human rights of the people is emerging as a frontline research area particularly in the North East region of India- the region which is more prone to the gushing impacts of global climate change due to its geo-ecological delicateness, shared international boundaries, rugged mountain terrains and the regions’ high dependency on climate sensitive livelihood such as agriculture, tea and forestry. Profound impacts of climate change in the region are being seen already in the form of rise in average temperature, slight changes in rainfall patterns, changes in the biodiversity, decrease of air quality which has ultimately threatened an array of human rights of the people in the region such as their Right to Life, Right to Food, Right to health and so on. Although Climate change has a cascading effect upon the people of the North East region of India, this area has been under-acknowledged and under-researched. Therefore, the paper purports to explore and evaluate the threats that anthropogenic climate change has imposed upon the human rights of the people of North-East India along albeit interrogating the measures that have been undertaken to address the issue and their efficacy.

The Analysis of Aerosol Distribution Using Modis AOD (Aerosol Optical Depth) With SARA (Simplified High Resolution Modis Retrieval Algorithm) for Air Quality Monitoring On 2017 (Study Case: Surabaya City, Indonesia)

Aerosols are particles suspended in the atmosphere that can affect human’s health, air quality, and climate. In monitoring the decrease in air quality due to aerosol distribution, it can be performed by calculating the thickness of the aerosol distribution. SARA (Simplified High-Resolution MODIS Aerosol Retrieval Algorithm) is the algorithm to calculate the distribution of AOD (Aerosol Optical Depth) values in Surabaya city using MODIS imagery. The distribution of AOD is calculated by considering the meteorological and land use aspects. The validation process is performed using ground-based measurements, i.e., the SPM and aerosol products at MODIS Level 2. The results of the AOD distribution vary in each month during 2017. The highest value of 1.794 µm occurred in May with a 7.0 mm/day rainfall in Dukuhpakis District. In comparison, the lowest AOD value of 0.494 µm occurred in January, with rainfall of 21.0 mm/day in Benowo District. The accuracy test result shows that the city of Surabaya is in the high category by reaching the value of 86%. While the accuracy-test with SPM data has a moderate level of correlation, giving the value of 42% with an RMSE value of 0.077 µm. The distribution of AOD is strongly influenced by the rainy season, proven by the correlation value between AOD and rainfall data reaching ~ 0.545. The correlation value between AOD and others meteorological and land use aspects is reaching ~ 0.391 for wind aspect and ~ 0.190 for land use.

Assessment on the Use of Meteorological and Social Media Information for Forest Fire Detection and Prediction in Riau, Indonesia

In this study, tweets related to fires in Riau, Sumatra, were identified using carefully selected keywords for the 2014–2019 timeframe. The TAGGS algorithm was applied, which allows for geoparsing based on the user’s nationality and hometown and on direct referrals to specific locations such as name of province or name of city in the message itself. Online newspapers covering Riau were analyzed for the year 2019 to provide additional information about the reasons why fires occurred and other factors, such as impact on people’s health, animal mortality related to ecosystem disruption, visibility, decrease in air quality and limitations in the government firefighting response. Correlation analysis between meteorological information, Twitter activity and satellite-derived hotspots was conducted. The existing approaches that BMKG and other Indonesian agencies use to detect fire activity are reviewed and a novel approach for early fire detection is proposed based on the crowdsourcing of tweets. The policy implications of these results suggest that crowdsourced data can be included in the fire management system in Indonesia to support early fire detection and fire disaster mitigation efforts.

The Effects of Air Quality on Hospital Admissions for Chronic Respiratory Diseases in Petaling Jaya, Malaysia, 2013–2015

This study assesses the impact of a decrease in air quality and the risk of hospital admissions to a public hospital for chronic respiratory diseases for residents of Petaling Jaya, a city in the Greater Kuala Lumpur area in Malaysia. Data on hospital admissions for asthma, bronchitis, emphysema and other chronic obstructive pulmonary disease, weather conditions and the Malaysian Air Pollution Index, a composite indicator of air quality, were collated. An unconstrained distributed lag model to obtain risk of hospitalization for a 10 μg/m3 increase in the API. The lag cumulative effect for a 10 μg/m3 increase in the API was calculated to test for harvesting in the short term. Findings indicate that after an initial decrease in admissions (days 3 and 4), admissions increased again at day 7 and 8 and this relationship was significant. We therefore conclude that a 10 μg/m3 increase has a greater effect on admissions for respiratory health in the short term than a harvesting effect alone would suggest. These results suggest that while air quality is improving in the Greater Kuala Lumpur area, no level of air pollution can be deemed safe.

Selecting Plant Types to Control Air Pollution and Developing Software to Plan Green Open Space in the Urban Area

The decrease in air quality becomes one of the development impacts that must be controlled deliberately. Planting trees is crucial for reducing the total number of dust and gas pollutants as it can restrain air pollution through absorption and adsorption. This research aimed at (a) determining the most appropriate types of plants for controlling air pollution and (b) developing software to plan the need for Green Open Space (GOS) in the urban city. The criteria of assessment for measuring the ability of a plant to absorb pollutant gas consisted of: (a) the density level of tree-crown, (b) plant combination, (c) thickness level of leaf, (d) the total number of leaves, and (e) plant distance. Meanwhile, the criteria for assessing the ability of the plant to adsorb dust particles comprised: (a) the roughness of leaf surface structure, (b) leaf width, (c) density level of tree-crown, (d) texture of stem skin surface, and (e) density level of the twig. The results of the research demonstrated that plants of Angsana (Pterocarpus indicus), Kihujan (Samanea saman), and Acacia (Acacia auriculiformis) were very appropriate for absorbing pollutant gas. Meanwhile, the good plants for adsorbing dust particles involved Angsana, Kihujan, Acacia, Tanjung (Mimusops elengi), Kersen (Mutingia calabura), Ketapang (Terminalia cattapa), and red Dadap (Erythrina crista-galli) by the level of adsorption ability around 65-75%. The successfully developed software could present suitability between Green Open Space (GOS) calculated manually and the one counted by software.

Air Quality Monitoring System in South Tangerang Based on Arduino Uno: From Analysis to Implementation

Pollution is a major factor in reducing air quality. A decrease in air quality can cause many problems such as global climate change, environmental damage, asthma and lung cancer. In this paper, we present an air quality monitoring system based on the Arduino Uno at Tangerang City. Air quality measured was carbon monoxide (CO), butane (C4H10), particulate matter (PM25) and ozone (O3). The types of sensors used were MQ-131 for the ozone sensor (O3), MQ-7 for carbon monoxide (CO), GP2Y1010AU0F for the particulate matter (PM25), and MQ-2 for butane (C4H10). This tool was built using Arduino and data from the sensor was sent to a Liquid Crystal Display (LCD) which visualized in numeric form. Data collection was carried out at Jalan Cabe Raya Pondok Cabe, South Tangerang. The test results showed the carbon monoxide (CO) had an average concentration of 2.79 ppm, the ozone gas concentration (O3) was 0.08 ppm particulate matter concentration (PM25) was 35.51 µg/m3 and butane average concentration (C4H10) was 35.05 ppm. Based on the data obtained, South Tangerang, Indonesia had a high level of pollution in particulate matter (PM 2 5).

PENERAPAN METODE UNIVERSAL KRIGING (UK) UNTUK PREDIKSI KONSENTRASI PARTICULATE MATTER 2.5 (PM2.5) DI KOTA BANDUNG

The tendency of increasing industrial and population activities in the city of Bandung causes a decrease in airquality. One material that causes air pollution which is very dangerous for health is particulate matter 2.5(PM2.5). Efforts to control and overcome PM2.5 concentration are carried out through measurements in severallocations. However, due to limitations of measuring devices and costs, so not all locations in the city ofBandung can be measured by PM2.5 concentration. Therefore it is necessary to interpolate the PM2.5 concentrationdata using the Universal Kriging method. In this paper, the Universal Kriging method can be applied to predictPM2.5 concentrations in locations that are not sampled because the observed PM2.5 concentration data arespatial data that have different PM2.5 concentrations at each location. Data were analyzed with the help ofsoftware R 3.5.3 and ArcGIS 10.4 to create contour maps. Based on the prediction results of PM2.5concentrations at unobserved locations obtained a first-order trend equation is better used to predictPM2.5 concentrations because it has a minimum Universal Kriging error variance value compared to thesecond-order trend equation. The results showed that the location with high PM2.5 concentration was in thenorthwest of Bandung City. Contour maps show locations that have low to high PM2.5 concentrations.

Spatial modelling of particular matter 10 distribution in Bandung City

Air pollution is a significant problem that occurs in urban areas. Bandung city, as a metropolitan city, continues to experience an increase in population growth, which has led to an increase in the built-up area. The increased built-up area will lead to a decrease in the area of green, which was causing a decrease in air quality. The study aims to analyze the spatial distribution of PM 10 in Bandung city and its relationship with the distribution of land surface temperature, building density, and vegetation density. The research used pollutant levels data and Landsat 8 Imagery in the dry month of 2018. The analysis used were IDW interpolation, LST, NDBI, NDVI, correlation, and statistical regression tests. This research concluded that PM 10 distribution model in the dry month of 2018, high pollutant levels are in the characteristics of the region with high surface temperature and index density, and a low index of vegetation density while low pollutant levels are in the characteristics of the region with surface temperature and density index value low buildings, and a high index value of vegetation density. Correlation results indicate an association with each variable. Based on the Pearson coefficient, the relationship between variables is at a moderate level. The regression results state that there is a significant influence between land surface temperature, vegetation index, and building index on the distribution patterns PM 10 in the Bandung city.

Influence of Indoor Climate on Employees in Office Buildings—A Case Study

The presented research work is aimed at investigation of the influence of indoor environmental conditions on employees in office buildings. Monitoring of carbon dioxide, temperature, relative humidity and pulse, as well as subjective evaluation, was carried out in three office rooms where air conditioning systems ensured the required amount of fresh air. Investigation showed that in two offices (A and B), the amount of fresh air did not comply with EN 15251:2017. The concentration of CO2 in office A was above 1000 ppm for 72% of the total length of stay. Respondents confirmed fatigue and headaches. In offices A and B, where CO2 concentration was around 1000 ppm, people with a weight of up to 70 kg experienced a significant increase in air temperature as well as odor. Persons with weight higher than 75 kg experienced a slight decrease in air quality. In office C, where CO2 concentration was around 800 ppm, respondents reported a slight decrease in air quality. According to pulse monitoring, it can be stated that in an office where there is an insufficient supply of fresh air, the pulse of a person falls or only slightly rises. A decrease in pulses may indicate the attenuation or stunning of people caused by poor air quality.

Major air pollutants seasonal variation analysis and long-range transport of PM10 in an urban environment with specific climate condition in Transylvania (Romania)

The air quality decrease, especially in urban areas, is related to local-scale conditions and to dispersion of air pollutants (regional and long-range) as well. The main objective of this study was to decipher the seasonal variation of PM10, NO, NO2, NOx, SO2, O3, and CO over a 1-year period (2017) and the possible relationships between air pollution and meteorological variables. Furthermore, trajectory cluster analysis and concentration-weighted trajectory (CWT) methods were used to assess the trajectories and the source-receptor relationship of PM10 in the Ciuc basin Transylvania, known as the “Cold Pole” of Romania. The pollutants show lower concentrations during warmer periods, especially during summer, and significantly higher concentrations were observed on heating season in winter due to seasonal variations in energy use (biomass burning) and atmospheric stability. Subsequently, in February, the highest concentration of PM10 was 132 μg/m3, which is 4 times higher than the highest recorded monthly mean. Our results indicate a negative correlation between CO/temperature (− 0.89), NOx/temperature (− 0.84) and positive between NOx/PM10 (0.95), CO/PM10 (0.9), and NOx/CO (0.98), respectively. Dominant transport pathways were identified and the results revealed that slow-moving southerly (~ 45%) and northwesterly (~ 32%) air masses represent almost 80% and mainly regional flows were discerned. During 2017, increased PM10 levels were measured at the study site when air masses arrived mostly from northwest and southeast. The CWT and polarplot models show a strong seasonal variation and significant differences were observed between weekdays and weekends, namely highest PM10 concentrations during weekends at low wind speed (2–4 m/s).

Risk Communication in the Air Pollution

Background: Air pollution is the leading cause of death, cognitive impairment, and work productivityso that it inhibits domestic investment and economic growth. Emissions of transportation activities, fueluse household and industry are the biggest sources of pollutants that require a strategy and action planholistically, to deal with the environmental crisis that is the cause decrease in air quality in Indonesia.Material and Method: Literature review sources come from an online journal database published byPubMed, Proquest, Google Scholar, and other sources such as the report on air pollution. Data of alldocuments collected manually and systematically.Findings and Discussion: Communicating air pollution is very important as a warning of its effects and alsocarries out risk management and prioritizes and mitigates risks in public and environmental health decisionmakingspaces. a review of published studies spanning impact on your health suggests that strategies forpreventing the effects of air pollution. However the evidence is base on the studies that are methodologicallyweak and have little or no underlying theory.Conclusion: The paper concludes with a call for more regions studies to evaluate the role of risk managementstrategy (with or without improved technologies) to reduce air pollution and burden disease in developingcountries as well as interventions that n draw more strongly on existing risk environment theory and practice.

Deep Random Subspace Learning: A Spatial-Temporal Modeling Approach for Air Quality Prediction

Decrease in air quality is one of the most crucial threats to human health. There is an imperative and necessary need for more accurate air quality prediction. To meet this need, we propose a novel long short-term memory-based deep random subspace learning (LSTM-DRSL) framework for air quality forecasting. Specifically, we incorporate real-time pollutant emission data into the model input. We also design a spatial-temporal analysis approach to make good use of these data. The prediction model is developed by combining random subspace learning with a deep learning algorithm in order to improve the prediction accuracy. Empirical analyses based on multiple datasets over China from January 2015 to September 2017 are performed to demonstrate the efficacy of the proposed framework for hourly pollutant concentration prediction at an urban-agglomeration scale. The empirical results indicate that our framework is a viable method for air quality prediction. With consideration of the regional scale, the LSTM-DRSL framework performs better at a relatively large regional scale (around 200–300 km). In addition, the quality of predictions is higher in industrial areas. From a temporal point of view, the LSTM-DRSL framework is more suitable for hourly predictions.

Implementation of Wireless Sensor Network (WSN) to calculate air pollution index of Samarinda City

Environmental pollution is one of the major problems opened by the world today. The main problem of this pollution, is decreasing air quality. This decrease in air quality is caused by many things, from excessive environmental exploitations, industrial activity, energy use, until the exhaust emissions of vehicles all of which trigger into pollution problems. So far Indonesian government has purchased and use the Air Quality Monitoring Base Station to measure different types of air pollutants. However, field statistics data show that the minimum quality of air quality measuring devices in each city is not commensurate with the minimum recommended amount, ideally one base station is used for 200.000 inhabitans. In this research, we developed an integrated Wireless Sensor Network to monitor the concentration of air pollution. Implemented fifteen sensor nodes which each node contains CO and NO2 sensors to sensing from the environment. the collected data will be used to calculate the standard air pollution index of samarinda city. The data is stored in server and we can publish for public to access information throught a gadget and thing speak server.

Distribution analysis of nitrogen dioxide (NO2) and ozone (O3) in Medan city with Geographic Information System (GIS)

The development of industry and the increase of transportation mode are directly proportional to the decrease in air quality. Some important pollutants have a negative impact on human health and the environment such as nitrogen dioxide (NO2) and ozone (O3). Distribution of NO2 and O3 concentrations from various sources of emissions in urban areas will provide basic information in the determination of policies, programs and regulations related to air pollution control. This study aims to map and analyze the concentration distribution of NO2 and O3 pollutants in Medan City. This research was conducted by a manual sampling of NO2 and O3 at 12 (twelve) points in Medan City. The sampling of NO2 and O3 concentrations is using impinger and then analyzed in a laboratory. The results showed that NO2 pollutant concentration in Medan City ranged from 21.33-306.19 |ig/m3. Meanwhile, for O3 concentrations ranged from 19.7-205.8 |ig/m3. Mapping of NO2 and O3 concentration using Geographic Information System obtained area that has the highest concentration of both NO2 and O3 is in Sub District of Medan Belawan. Furthermore, efforts to control air pollution can be done by applying clean technology to industry and encouraging the use of mass transportation.

Pengaruh Kualitas Lingkungan dan Motivasi pada Kinerja Akademik Siswa SMA

Environment (external factors) and user attributes (internal factors) affect the user's response to the environment. This study aims to uncover how the quality of the environment in high school as an external factor and student motivation as an internal factor affects the academic performance of the students. The research was conducted with qualitative and quantitative approach. Data were collected by survey using an online questionnaire. From the regression analysis, it was found that the dimensions of external factors of "classroom order", "social interaction and supervision" and "air quality" further increased the effectiveness of student learning rather than internal dimensions of "resilience and capacity building". Meanwhile, the decrease in air quality, noise, and environmental pollution can cause student learning disruption. Based on these findings, it can be concluded that the learning environment plays an important role in shaping the effectiveness of student learning.

Determine Environment Impacts in Upstream Processes of Oil and Gas Industries

Indonesia's population increasing from 238.5 million to 305.6 million from 2010-2035 cause increase of energy supply to meet energy consumption needs and also correlate with global warming. One of them is from the oil and gas sector. The process of it production is divided into upstream process which starts from hydrocarbon extraction to become a ready-to-sell product and downstream process comprise of processing to consumer consumption. The research objective of this paper is to find out the upstream process of oil and gas industry as well as the impact that may be caused. The upstream process of the oil and gas industry begins with land clearing, hydrocarbon drilling / extraction from within the earth, feasibility testing, development and production, distribution, storage and distribution possible to the first consumer. From these activities, the impacts are caused by the decrease of air quality mainly due to the burning of flare gas and tool mobilization, the decrease of water quality, and the deterioration of soil quality.