Indoor–outdoor concentrations of particulate matter in nine microenvironments of a mix-use commercial building in megacity Delhi

Abstract

Three naturally and six mechanically ventilated microenvironments (MEs) of a mixed use commercial building in Delhi are used to study indoor-outdoor (I/O) relationships of particulate matter ≤10µm (PM 10 ), ≤2.5µm (PM 2.5 ) and ≤1µm (PM 1 ). Effect of environmental and occupancy parameters on the concentrations of PM during working and non-working hours (i.e. activity and non-activity periods, respectively) are also investigated. Average outdoor concentration of PM 10 and PM 2.5 were found to exceed the 24 hour averaged national standard values, showing a polluted environment surrounding the studied building. During the working hours, indoor PM 10 concentration was found 6-10 times, both PM 2.5 and PM 1 were 1.5-2 times, higher than the non-working hours in the selected MEs. The variations of indoor concentrations were highest (17.1-601.2 µg/m 3 ) for PM 10 compared with PM 2.5 (16.9-102.6 µg/m 3 ) and PM 1.0 (10.6-63.6 µg/m 3 ). The I/O for PM 10 , PM 2.5 and PM 1.0 varied from 0.37-3.1, 0.2-3.2 and 0.17-2.9, respectively. The results suggest highest I/O for PM 10 , PM 2.5 and PM 1 as 3.1, 2.15 and 1.76, respectively, in all the three natural ventilated MEs (canteen, kitchen, reception). Irrespective of PM types, the average I/O was <1 for mechanically ventilated MEs compared with >1 for naturally ventilated MEs. As opposed to PM 1 , better correlation (r >0.6) was noted between indoor PM 10 , PM 2.5 and CO 2 concentrations in most of the airtight MEs.

Assessment of Indoor-Outdoor Particulate Matter Air Pollution: A Review

“…A similar temporal variation between occupancy and non-occupancy is also found in a previous study by Branis et al [56] which, however, developed the study using different sampling strategies and time references as discussed later in this review. High variability of PM 2.5 I-O concentrations is also described by other analysed studies [40,50,[57][58][59][60][61]. In particular, the findings of Liu et al [58] through different residential and commercial buildings in Beijing, clearly show the wide variability within indoor PM 10 and PM 2.5 concentrations which are, resultantly, higher in restaurants, dormitories, and classrooms, rather than in supermarkets, computer rooms, offices, and libraries (PM 10 and PM 2.5 ranging, respectively, from 373.8 µg/m 3 and 136.6 µg/m 3 in restaurants to 33.8 µg/m 3 and 5.6 µg/m 3 in libraries).…”

“…More recently, Diapouli et al [54] monitored I-O mass and PN concentrations of UFP, black smoke, PM10, and PM2, the latter using a custom-made impactor (with a cut-off point at 2.1 μm and at 23 L/min). The use of PM1 is documented in three studies at schools and universities of Central Europe and in studies assessing multiple indicators (including TSP, PM10, and PM2.5) [51,[56][57][58]63,64].…”

Size–segregated particulate matter inside residences of elderly in the Metropolitan Area of São Paulo, Brazil

“…The elderly residences have only natural ventilation and their I/O mean is 1.06 (Figure 5b). Goyal and Kumar (2013) showed I/O ratio for PM 10 in kitchen and canteen with natural ventilation as 1.33 and 1.47, respectively. In our elderly residences, the I/O rate is lower, presumably because of the measurements being carried out in the living room with no direct sources of PM 10 emissions.…”

“…As summarized in Table 1, there are limited studies worldwide that focus on the size-resolved particles in indoor environments and most of the focus usually remains on PM 10 and PM 2.5 (Chao and Wong, 2002;McCormack et al, 2008) or include PM 1 at the maximum (Jones et al, 2000;Goyal and Kumar, 2013;Viana et al, 2014). Studies for indoor elderly environments focusing on size-segregated particles are yet limited.…”

Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model

“…ANN models are capable of fast processing with several input and output variables (Lal and Tripathy 2012;Kakosimos et al 2011;Bose and Liang 1998;Anderson 1995). ANN models have been very accurate in many environmental health application areas, especially indoor environment (Skön et al 2012;Kassomenos et al 2011), air quality forecasting Goyal and Kumar;Alekseev and Seixas Citation details: Patra, A., Gautam, S., Majumdar, S., . Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model.…”