A study of atmospheric particulate matter in a city of the central region of Brazil using time-series analysis

Lima, Euclides Antônio Pereira de; Guimarães, Ednaldo Carvalho; Pozza, Simone Andréa; Barrozo, Marcos A.S.; Coury, José Renato

doi:10.1504/ijee.2009.026444

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…The rapid development of cities around the world, aided by the industrial acceleration of the last two decades, has had a major impact on the environment, including in the city of Makassar, Indonesia. This is the fastest growing city in Eastern Indonesia, and the impact of the water, soil, and air pollution caused by this growth can severely affect the health of its residents [1], [2], [3], [4]. Particle pollution and its impact in Makassar city has been reported by research [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision Analysis for Selecting Waste Composting Technology in Makassar, Indonesia

Yunus¹,

Muis²,

Anggraini³

et al. 2020

Journal of Southwest Jiaotong University

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The city of Makassar is in a coastal area. Cambayya village in Makassar is densely populated and has complex waste problems. Limited land access and narrow road access make it difficult to transport waste. People’s habit of throwing garbage into the sea leads to the accumulation of garbage on the coast of Cambayya and causes various forms of environmental pollution. A form of waste management that uses composting methods is needed to handle organic waste. This study aims to find an alternative piece of composting technology to handle waste coastal area conditions. This study uses qualitative methods, namely Analytical Hierarchy Process which is descriptive, refers to data and utilizes theory as a support. The sample is chosen using the purposive sampling method. Furthermore, the analytical hierarchy process approach and a comparison matrix involving four criteria are used, namely, economic, social, environmental and technical criteria. Thirteen sub-criteria are also used. Stakeholders from the government, community, and academia are interviewed, and direct observation and documentation (evidence in data collection during interviews) are used. This study found that communal composting technology is the most appropriate type of composting for coastal areas such as Cambayya, Makassar, because the amount of waste processed is higher than other technologies such as individual and hallway composter. This reduces the amount of waste disposed at the final waste disposal site, transforming community waste management habits, saving time, increasing the community’s income from the sale of compost.

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Section: Introductionmentioning

confidence: 99%

A Multi-Criteria Decision Analysis for Selecting Waste Composting Technology in Makassar, Indonesia

Yunus¹,

Muis²,

Anggraini³

et al. 2020

Journal of Southwest Jiaotong University

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“…Of the applied parametric methods, the most popular for times series analysis are the autoregressive integrated moving average (ARIMA) methods, also known as stochastic Box-Jenkins methodology (Box et al, 1994). In Lima et al (2009), both total suspended particles and particles with a diameter equal or smaller than 10 μm (PM10) are analysed using the seasonal ARIMA approach. The Box-Jenkins methodology and multivariate analysis have been applied (Liu, 2009) for simulation and forecasting of the daily average PM10 concentrations.…”

Section: Introductionmentioning

confidence: 99%

Assaying SARIMA and generalised regularised regression for particulate matter PM10 modelling and forecasting

Gocheva-Ilieva

Ivanov

2019

IJEP

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Two different predictive modelling approaches -classical SARIMA time series methodology and the new Generalised PathSeeker (GPS) regularised regression method, supported by stochastic gradient boosting trees, RuleLearner and other data mining techniques -are used to examine the concentration of particulate matter PM10 in the town of Kardzhali, Bulgaria. Empirical models are developed to simulate and forecast pollution levels based on hourly PM10 data from 1 January 2011 to 28 February 2014 in dependence on six meteorological variables. The constructed models have been used for 5-days-ahead hourly forecasts, compared to actual data from 1 to 5 March 2014. The obtained SARIMA and GPS models fit very well to historical data with coefficients of determination R 2 = 90% and 82% and root mean square error RMSE = 0.114 and 0.151, respectively. In forecasting, the GPS models outperform SARIMA approach. This could be explained by the preliminary classification provided by the data mining techniques and cross-validation procedure.

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“…Studies on the influence of air pollution on the quality of human life have been conducted with great intensity in recent years [1]- [11]. This study aimed to evaluate the effects of variables that affect air quality, and of which, some made a direct link between air pollution with mortality rates and respiratory diseases.…”

Section: Introductionmentioning

confidence: 99%

Development of a Short-Term Ozone Prediction Tool in Campo Grande-MS-Brazil Area Based on Meteorological Variables

Souza¹,

Aristones²,

Pavão³

et al. 2014

OJAP

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The objective of this study was to develop a model to predict the concentration of ozone. The measurements of ozone were conducted at the Federal University of Mato Grosso do Sul. The predictor variables related to climate were supplied by Embrapa Gada de Corte. Analyses of the data show a strong correlation between ozone and wind speed (0.75), humidity (−0.64) and temperature (0.41), as the factors that affect levels of concentration ozone. The observed and predicted values of daily ozone had a correlation coefficient (R) of 0.69.

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A study of atmospheric particulate matter in a city of the central region of Brazil using time-series analysis

Cited by 10 publications

References 17 publications

A Multi-Criteria Decision Analysis for Selecting Waste Composting Technology in Makassar, Indonesia

A Multi-Criteria Decision Analysis for Selecting Waste Composting Technology in Makassar, Indonesia

Assaying SARIMA and generalised regularised regression for particulate matter PM10 modelling and forecasting

Development of a Short-Term Ozone Prediction Tool in Campo Grande-MS-Brazil Area Based on Meteorological Variables

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