scholarly journals Extracting relationship between air pollution and precipitation using spatio-temporal analysis in Tehran metropolis

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Shokouh Dareshiri ◽  
Mohammadreza Sahelgozin ◽  
Maryam Lotfian ◽  
Jens Ingensand
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Regression Model ◽  
Influencing Factors ◽  
Polynomial Regression ◽  
Human Life ◽  
Pearson Correlation ◽  
Influencing Factor ◽  
Polynomial Equation ◽  
Temporal Analysis

<p><strong>Abstract.</strong> Precipitation is one of the main stages of the water cycle, and it is required for the organisms to survive on the planet. In contrast, air pollution is a phenomenon that has greatly affected the human life nowadays. Population growth, development of factories and increasing number of fossil fuel vehicles are the most influencing factors on air pollution. In addition to understand nature of precipitation and air pollution, finding relationship between these two phenomena is necessary to make appropriate policies for reducing air pollution. Furthermore, studying trends of precipitation and air pollution in the past, is helpful to forecast the times and places with less precipitation and more air pollution for a better urban management. In this study, we tried to extract any probable relationship between these two parameters by investigating their monthly measured amounts in 22 municipal districts of Tehran in three epochs of time (2009, 2013 and 2017). Carbon Monoxide (CO) was considered as the indicator of air pollution. Results of the study show that the parameters have a significant relationship with each other. By using Pearson Correlation Coefficient and One-Way Variance (ANOVA) test, relationship between the data for each month and for each district of Tehran were studied separately. As the time has passed and the air pollution has increased, the correlation between the parameters in districts has decreased. In addition, during the cold months of the year, the correlations decrease since the fact that precipitation is not the only influencing factor on the air pollution due to the rise of air “Inversion”. Finally, the polynomial regression model of carbon monoxide based on precipitation was extracted for each of the three years. The model suggests a degree three polynomial equation. The obtained coefficients from the regression model show that the relationship between parameters was stronger in the years with more rainfalls. This can be due to the more significant impact of other influencing factors on air pollution, such as population density, wind direction, vehicles and factories in the areas or conditions with a less rainfall.</p>

scholarly journals Artikel Review: Produksi Gas Hidrogen dari Reaksi Elektrolisis Sebagai Bahan Bakar Non-Fosil

UNISTEK ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 30-35
Author(s):  
Ismi Nurlatifah ◽  
Lily Arlianti
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
Human Life ◽  
Environmentally Friendly ◽  
Hydrogen Gas ◽  
Energy Resources ◽  
Hydrogen Fuel ◽  
Clean Environment

In carrying out various activities today, it cannot be separated from the fuel. As we all know, fuels that are still commonly used today are fossil fuels whose energy resources are running low. Not only that, fossil fuels have also been shown to produce air pollution. Unhealthy air conditions can certainly reduce human life expectancy. In order to make the clean environment and not polluted by the air pollution, there must be environmentally friendly fuels. The answer for this kind of fuels is hydrogen which comes from nonfossil. One way to obtain hydrogen is an electrolysis reaction. Water can produce hydrogen through electrolysis. Just a few liters of water, it can produce ten to twenty thousand liters of hydrogen gas per hour. The use of Hydrogen as a non-fossil fuel has been proven to be environmentally friendly and free of carbon monoxide. Healthy air and a clean environment are certainly our responsibility. It's time to switch by using hydrogen fuel.

scholarly journals Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

2022 ◽  
Vol 14 (2) ◽  
pp. 252
Author(s):  
Nan Lin ◽  
Ranzhe Jiang ◽  
Qiang Liu ◽  
Hang Yang ◽  
Hanlin Liu ◽  
...  
Keyword(s):  
Land Cover ◽  
Regression Model ◽  
Influencing Factors ◽  
Ridge Regression ◽  
Influencing Factor ◽  
Hydrologic Cycle ◽  
Water Evaporation ◽  
Land Cover Types ◽  
Spatiotemporal Trends ◽  
River Plain

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.

scholarly journals On Modeling of COVID-19 for the Indian Subcontinent using Polynomial and Supervised Learning Regression

2020 ◽  
Author(s):  
Dishita Neve ◽  
Honey Patel ◽  
Harsh S. Dhiman
Keyword(s):  
Polynomial Regression ◽  
Indian Subcontinent ◽  
Human Life ◽  
Pearson Correlation ◽  
Series Representation ◽  
Bayesian Regression ◽  
Support Vector ◽  
The Novel ◽  
Root Cause ◽  
Highly Correlated

AbstractCOVID-19, a recently declared pandemic by WHO has taken the world by storm causing catastrophic damage to human life. The novel cornonavirus disease was first incepted in the Wuhan city of China on 31st December 2019. The symptoms include fever, cough, fatigue, shortness of breath or breathing difficulties, and loss of smell and taste. Since the devastating phenomenon is essentially a time-series representation, accurate modeling may benefit in identifying the root cause and accelerate the diagnosis. In the current analysis, COVID-19 modeling is done for the Indian subcontinent based on the data collected for the total cases confirmed, daily recovered, daily deaths, total recovered and total deaths. The data is treated with total confirmed cases as the target variable and rest as feature variables. It is observed that Support vector regressions yields accurate results followed by Polynomial regression. Random forest regression results in overfitting followed by poor Bayesian regression due to highly correlated feature variables. Further, in order to examine the effect of neighbouring countries, Pearson correlation matrix is computed to identify geographic cause and effect.

scholarly journals Perancangan dan Implementasi Alat Pengukur Tingkat Polusi Udara Karbon Monoksida dan Debu Berbasis Website Menggunakan Raspberry Pi

2021 ◽  
Vol 4 (1) ◽  
pp. 69
Author(s):  
Riza Samsinar ◽  
Ichsanul Fikri ◽  
Fadliondi Fadliondi
Keyword(s):  
Carbon Dioxide ◽  
Air Pollution ◽  
Carbon Monoxide ◽  
Human Life ◽  
Ultra Violet ◽  
Raspberry Pi ◽  
Ultraviolet Rays ◽  
Measuring Device ◽  
Pollution Levels ◽  
Carbon Dioxide Co2

Udara merupakan unsur terpenting  dalam kehidupan manusia. polusi udara      muncul menjadi masalah yang serius di kota-kota besar, polutan yang ada di udara   tersebut berbahaya bagi kesehatan manusia dan lingkungan. Udara yang berada disekeliling bumi yang fungsinya sangat penting bagi kehidupan di dunia ini. Dalam udara terdapat unsur oksigen (O2) untuk bernafas, karbon dioksida (CO2) untuk proses fotosintesis oleh klorofil pada daun dan ozon (O3) untuk menahan sinar ultra violet. Susunan (komposisi) udara bersih dan kering, tersusun oleh: Nitrogen (N2) 78,09%, Oksigen (O2) 21,94%, Argon (Ar) 0,93%, Karbon dioksida 0,032%. Untuk menyelesaikan masalah tersebut membuat perancangan dan implementasi alat pengukur tingkat polusi udara karbon monoksida dan debu berbasis website menggunakan raspberry pi. Bertujuan untuk mengetahui kadar polusi udara karbon monoksida dan debu. Supaya dapat dimonitoring melalui handphone dan laptop. Hasil dari alat perancangan dan implementasi alat pengukur tingkat polusi udara karbon monoksida dan debu. Untuk mengukur sebuah karbon monoksida dan mengukur debu pada polutan di luar ruangan seperti jalan raya yang dapat dimonitoring melalui handphone dan laptop. Dengan pengujian pada waktu berangkat kerja, waktu makan siang, dan waktu pulang kerja.Air is the most important element in human life. Air pollution appears to be a serious problem in big cities, the pollutants in the air are harmful to human health and the environment. The air that surrounds the earth whose function is very important for life in this world. In the air there are elements of oxygen (O2) for breathing, carbon dioxide (CO2) for photosynthesis by chlorophyll in leaves and ozone (O3) to withstand ultraviolet rays. Composition (composition) of clean and dry air, composed of: Nitrogen (N2) 78.09%, Oxygen (O2) 21.94%, Argon (Ar) 0.93%, Carbon dioxide 0.032%. To solve this problem, we designed and implemented a website-based carbon monoxide and dust level measuring device using raspberry pi. Aims to determine air pollution levels of carbon monoxide and dust. So that it can be monitored via cellphones and laptops. The results of the tool design and implementation of measuring devices for air pollution levels of carbon monoxide and dust. To measure a carbon monoxide and measure dust in outdoor pollutants such as roads that can be monitored via cellphones and laptops. By testing on the time to go to work, lunch time, and time to come home from work.

Environmental and Hygienic Assessment of Ambient Air Pollution in Oil-Producing and Oil-Refining Areas of the Republic of Bashkortostan and Population Health Status

2020 ◽  
pp. 26-32
Author(s):  
Z.B. Baktybaeva ◽  
R.A. Suleymanov ◽  
T.K. Valeev ◽  
N.R. Rahmatullin ◽  
E.G. Stepanov ◽  
...  
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Health Status ◽  
Nitrogen Dioxide ◽  
Population Health ◽  
Respiratory Diseases ◽  
Ambient Air ◽  
Oil Refining ◽  
Disease Rates ◽  
Total Suspended Particles

Introduction. High density of oil-producing and refining facilities in certain areas of Bashkortostan significantly affects the environment including ambient air quality in residential areas. Materials and methods. We analyzed concentrations of airborne toxicants (sulfur and nitrogen oxides, nitrogen and carbon oxides, hydrogen sulfide, ammonia, xylenes, toluene, phenol and total suspended particles) and population health status in the cities of Ufa, Sterlitamak, Salavat, Blagoveshchensk, and the Tuymazinsky District in 2007–2016. Pearson's correlation coefficients (r) were used to establish possible relationships between medico-demographic indicators and air pollution. Results. Republican fuel and energy enterprises contributed the most to local air pollution levels. Gross emissions from such enterprises as Bashneft-Ufaneftekhim and Bashneft-Navoil reached 43.69–49.77 thousand tons of pollutants per year. The levels of some air pollutants exceeded their maximum permissible concentrations. Elevated concentrations of ammonia, total suspended particles, nitrogen dioxide, and carbon monoxide were registered most frequently. High rates of congenital abnormalities, respiratory diseases in infants (aged 0-1), general mortality and morbidity of the population were observed in some oil-producing and refining areas. The correlation analysis proved the relationship between the concentration of carbon monoxide and general disease rates in adults based on hospital admissions (r = 0.898), general incidence rates in children (r = 0.957), and blood disease rates in infants (r = 0.821). Respiratory diseases in children correlated with nitrogen dioxide emission levels (r = 0.899). Conclusions. Further development of oil-producing, petrochemical and oil-refining industries should be carried out taking into account socio-economic living conditions of the population.

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals The role of serum β-trophin and endostatin in patients with polycystic ovary syndrome: Are they correlated?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Gong ◽  
Aikmu Bilixzi ◽  
Xinmei Wang ◽  
Yanli Lu ◽  
Li Wan ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Influencing Factors ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Ovary Syndrome ◽  
Factors Affecting ◽  
Main Factors

Abstract Background It’s necessary to investigate the serum β-trophin and endostatin (ES) level and its influencing factors in patients with newly diagnosed polycystic ovary syndrome (PCOS). Methods Newly diagnosed PCOS patients treated in our hospital were selected, and healthy women who took physical examination during the same period as healthy controls. We detected and compared the related serum indicators between two groups, Pearson correlation were conducted to identify the factors associated with β-trophin and ES, and the influencing factors of β-trophin and ES were analyzed by logistic regression. Results A total of 62 PCOS patients and 65 healthy controls were included. The BMI, WHI, LH, FSH, TT, FAI, FBG, FINS, HOMA-IR, TC, TG, LDL, ES in PCOS patients were significantly higher than that of healthy controls, while the SHBG and HDL in PCOS patients were significantly lower than that of healthy controls (all p < 0.05). β-trophin was closely associated with BMI (r = 0.427), WHR (r = 0.504), FBG (r = 0.385), TG (r = 0.405) and LDL (r = 0.302, all p < 0.05), and ES was closely associated with BMI (r = 0.358), WHR (r = 0.421), FBG (r = 0.343), TC (r = 0.319), TG (r = 0.404, all p < 0.05). TG, BMI, WHR and FBG were the main factors affecting the serum β-trophin levels (all p < 0.05). FBG, TC and BMI were the main factors affecting the serum ES levels (all p < 0.05). The TG, β-trophin, ES level in PCOS patients with insulin resistance (IR) were significantly higher than that of those without IR (all p < 0.05). Conclusion Increased β-trophin is closely associated with increased ES in patients with PCOS, which may be the useful indicators for the management of PCOS.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

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