A B S T R A C TEstimates of Asian emissions of air pollutants and carbonaceous aerosols and their mid-term projections have been changing significantly in the last years. The remote sensing community has shown that increase in NO x in Central East Asia is much stronger than any of the emission inventories or projections indicated so far. A number of studies reviewing older estimates appeared. Here, we review the key contributions and compare them to the most recent results of the GAINS model application for Asia and to the SRES projections used in the IPPC work. The recent projections indicate that the growth of emissions of SO 2 in Asia should slow down significantly towards 2010 or even stabilize at the current level. For NO x , however, further growth is projected although it will be most likely slower that in the last decade, owing to introduction of measures in transport. Emissions of carbonaceous aerosols (black carbon and organic carbon) are expected to decline after 2010, largely due to reduced use of biofuels in residential sector and efficiency improvements. The estimates of these emissions are burdened with significantly larger uncertainties than SO 2 and NO x ; even for the year 2000 the differences in estimates between studies are up to a factor of 2.

Green building certifications aim to achieve sustainable buildings that are healthy, energysaving, and environmentally friendly. To construct healthy built environments for occupants, a high indoor environment quality (IEQ) has to be maintained. The goal of this paper is to analyze how and to what extent indoor air quality (IAQ), as a subset of IEQ, is taken into account in green building certifications worldwide. Thus, IAQ requirements were reviewed in 31 green building certifications from 30 countries worldwide. These certification programs include 13 countries in Asia, 9 in Europe, 5 in Americas, 2 in Oceania, and 1 in Africa. Fifty-five green building schemes were selected from among the 31 certifications. Rating systems are commonly used in green building schemes to evaluate the capability and level of a building to achieve lifecycle sustainability. The average contribution of IAQ to green building schemes worldwide is 2 7.5%. Volatile organic compounds (VOCs), formaldehyde, and carbon dioxide (CO2) are the indoor air pollutants most frequently considered. Ozone (O3) and semi-volatile organic compounds (SVOCs) are mentioned in less than 6.7% of the certifications worldwide. Emission source control, ventilation, and indoor air measurement are the three main pathways used in green building schemes for IAQ management. All of the certifications include ventilation as a way to manage IAQ. Emission source control is included in 77% of the certifications and is mainly targeted at building material emissions. Indoor air measurement is included in 65% of the certifications but may be optional.

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Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed.Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM 2.5 and PM 10 ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees. K E Y W O R D Sartificial neural networks, data mining, IAQ, partial least squares, particulate matter, regression | 705 WEI Et al.