Regression and multivariate models for predicting particulate matter concentration level

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies

Application of step wise regression analysis in predicting future particulate matter concentration episode

Particulate matter is an air pollutant that has resulted in tremendous health effects to the exposed populace. Air quality forecasting is an established process where air pollutants particularly, particulate matter (PM10) concentration is predicted in advance, so that adequate measures are implemented to reduce the health effect of PM10 to the barest level. The present study used daily average PM10 concentration and meteorological parameters (temperature, humidity, wind speed and wind direction) for 5 years (2006–2010) from three industrial air quality monitoring stations in Malaysia (Balok Baru, Tasek and Paka). Time series plot was used to assess PM10 pollution trend in the industrial areas. Additionally, step wise regression (SWR) analysis was used to predict next day PM10 concentrations for the three industrial areas. The SWR method was compared with a persistence model to assess its predictive capabilities. The results for the trend analysis showed that, Balok Baru (BB) had higher PM10 concentration levels, having high values in 2006, 2007 and 2009. These values were higher than the Malaysian Ambient Air Quality Guideline (MAAQG) of 150 μg/m3. Subsequently, the other two industrial areas Tasek (TK) and Paka (PK) had no record of violating the MAAQG. The results for the SWR analysis had significant R 2 values of 0.64, 0.66 and 0.60, respectively. The model performance results for variance inflation factor (VIF) were less than 5 and Durbin-Watson test (DW) had value of 2 for each of the study areas, which were significant. The comparative analysis between SWR and persistence model showed that the SWR had better capabilities, having lower errors for the BB, TK and PK areas. Using root mean square error (RMSE), the results showed error differences of 7, 12 and 16 %, and higher predictability using index of agreement (IA), having a difference of 17, 19 and 16 % for BB, TK, and PK areas, respectively. The results showed that SWR can be used in predicting PM10 next day average concentration, while the extreme event detection results showed that 100 μg/m3 were better detected than the 150 μg/m3 bench marked levels

Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models

Prediction of particulate matter (PM10) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analysed daily average PM10, temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006–2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM10 in two peculiar seasons: south-west monsoon and north-east monsoon seasons, using principal component analysis (PCA). Subsequently, lognormal regression (LR), multiple linear regression (MLR) and principal component regression (PCR) methods were used to forecast next-day average PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R2) and better performance indicator results than LR and MLR methods. The outcomes of this study signify that PCR models can be effectively used as a suitable format in predicting next-day average PM10 concentration levels

Biodegradation of high-strength palm oil mill effluent (POME) through anaerobes partitioning in an integrated baffled reactor inoculated with anaerobic pond sludge

Performance of a laboratory-scale integrated baffled reactor for the treatment of raw palm oil mill effluent (POME) was investigated. Initially, the reactor was fed with diluted POME (COD=1,830 mg/L and OLR=0.46 g COD/L Day) which was then increased gradually to actual concentration (COD=45,500 mg/L and OLR=11.38 g COD/L Day). Reactor operation was studied in two different hydraulic retention times (HRTs) (4 and 6 days) using POME with no effluent recycled feed and after alkalinity supplementation. Chemical oxygen demand (COD) removal of 79 and 83 % at an HRT of 4 and 6 days were attained at the highest organic loading rate (OLR=11.38 g COD/ L Day). The presence of Arcella-like and Metopus-like species and pH profile in the bioreactor’s compartments imply that anaerobic system is active in the reactor throughout the study. Use of methanogen-enriched inocula, smooth OLR augmentation, and appropriate separation of acidogens and methanogens in the reactor were the reasons for satisfactory performances of the system

Impact of construction waste minimization at construction site: case study

Waste reduction in the construction sector is essential not just from the position of overall performance, but also interests escalated recently about the detrimental impact of the waste of construction materials on the natural surroundings. The reuse and recycling of construction elements used to be substantial due to material scarcity during the war and post-war era, specifically when the price of virgin materials keep inflating. Nevertheless, the motivation to reuse and recycle construction materials has fallen ever since the flow of raw materials has grown more consistent and satisfactorily. This paper involves an ongoing sequence of waste audits at Universiti Teknologi Petronas (UTP) R&D construction sites. The methodology consists of discussion, observation and quantitative evaluations of the kinds and distribution of wastes. The finding of this study demonstrates that the project aimed at both minimizing the volume of waste generated and diverting as much waste as possible from landfill that contribute to the recycle with over 1200 tonnes of material which constitute about 73% of recycle and reused rate mainly from timber and metal

4D BIM application in AEC industry: impact on integrated project delivery

As project delivery approaches that are supported by Building Information Modelling (BIM) are continuously acknowledged throughout the Architecture, Engineering and Construction (AEC) industry, an innovative modelling approach called 4D BIM is starting to develop, which associates elements of 3D BIM with time and scheduling information. Traditional construction planning applications like bar charts and network diagrams fail to present and communicate the spatial and temporal or 4D components of construction schedules efficiently. As a result, they do not permit project managers to produce scheduling alternatives quickly to obtain the finest solution to develop a particular design. With 4D modelling, the whole period of a sequence of activities that is executed by those involved in the project can be presented visually. With the rising interest in BIM and the wider use of this and other technological innovations in the AEC industry, 4D BIM tools, which has been broadly employed, is becoming increasingly recognized among fundamental technological fields under BIM. This study aims to highlight and review numerous impacts of 4D applications on Integrated Project Delivery (IPD) in the AEC industry and suggest the ideal strategy to optimize the tools for rapid project delivery

Mengoptimumkan sistem anaerobik pada Reaktor Anaerobik Sesekat (ABR) untuk menghasilkan biogas dari sisa organik menggunakan perisian SuperPro Designer® / Zulhafizal Othman et al.

Dalam kajian ini, Sistem Reaktor Anaerobik Sesekat (ABR) telah digunakan. Penentuan jenis reaktor yang dapat memberikan hasil pengeluaran gas dan COD efluen yang optimum dijalankan. Penggunaan perisian SuperPro Designer digunakan untuk menentukan hasil COD keluaran dan komposisi gas metana yang terhasil. Kajian yang dilakukan ini hanya mengfokuskan kepada bilangan sesekat yang berlainan yang dapat mempengaruhi keberkesanan menghasilkan komposisi metana dan COD keluaran yang optimum. Bilangan sesekat yang dikaji ialah 3, 4, 5 dan 6 sesekat. Setelah melakukan simulasi dan nilai yang terhasil dibandingkan dengan nilai purata setiap kes untuk mendapatkan reaktor yang optimum. Keputusan ujikaji yang diperolehi ialah bagi kes sisa kumbahan dapur, didapati bahawa reaktor yang optimum adalah reaktor yang mempunyai 4 sesekat yang mana nilai COD keluaran dan komposisi gas metana adalah 32655 mg O/L dan 68 %. Bagi kes sisa perindustrian dan sisa buangan kelapa sawit, reaktor dengan 4 sesekat dikenalpasti sebagai reaktor paling optimum yang mana nilai COD keluaran sebanyak 8067 mg O/L dan 3900 mg O/L manakala komposisi gas metana adalah 33 % dan 37%. Dalam kes sisa najis binatang, didapati bahawa reaktor yang optimum adalah reaktor yang mempunyai 4 sesekat yang mana nilai COD keluaran dan komposisi gas metana adalah 30865 mg O/L dan 32 %

Current progress on removal of recalcitrance coloured particles from anaerobically treated effluent using coagulation–flocculation

The palm oil industry is the most important agro industries in Malaysia and most of the mills adopt anaerobic digestion as their primary treatment for palm oil mill effluent (POME). Due to the public concern, decolourisation of anaerobically treated POME (AnPOME) is becoming a great concern. Presence of recalcitrant-coloured particles hinders biological processes and coagulation–flocculation may able to remove these coloured particles. Several types of inorganic and polymers-based coagulant/flocculant aids for coagulation–flocculation of AnPOME have been reviewed. Researchers are currently interested in using natural coagulant and flocculant aids. Modification of the properties of natural coagulant and flocculant aids enhanced coagulation–flocculation performance. Modelling and optimization of the coagulation–flocculation process have also been reviewed. Chemical sludge has the potential for plant growth that can be evaluated through pot trials and phytotoxicity test