HVAC air-quality model and its use to test a PM2.5 control strategy

This paper presents a MATLAB^® Simulink air-quality model of a commercial building with a heating, ventilation, and air conditioning (HVAC) system in Fairbanks, Alaska. Outdoor and indoor real-time fine particulate matter (PM_2.5) levels were measured at this building during a summer wild-fire smoke episode and then during a winter period. The correlation coefficient between the model-predicted and the measured indoor concentrations was 0.99 for the summer and 0.98 for the winter, justifying the usability of the model for further studies. An HVAC control algorithm was developed that reduces the indoor PM_2.5 levels. The algorithm was tested using the HVAC Simulink model and the outdoor PM_2.5 data from the summer smoke episode. The average indoor PM_2.5 level with this control algorithm was 65% lower than with the regular control. Thanks to the PM_2.5 control strategy being automatically engaged only during episodes, it was shown to have the potential of significantly reducing the indoor PM_2.5 levels without significantly compromising the purpose of the original control strategy.     

Factors influencing relationships between personal and ambient concentrations of gaseous and particulate pollutants

Previous exposure studies have shown considerable inter-subject variability in personal-ambient associations. This paper investigates exposure factors that may be responsible for inter-subject variability in these personal-ambient associations. The personal and ambient data used in this paper were collected as part of a personal exposure study conducted in Boston, MA, during 1999-2000. This study was one of a group of personal exposure panel studies funded by the U.S. Environmental Protection Agency's National Exposure Research Laboratory to address areas of exposure assessment warranting further study, particularly associations between personal exposures and ambient concentrations of particulate matter and gaseous co-pollutants. Twenty-four-hour integrated personal, home indoor, home outdoor and ambient sulfate, elemental carbon (EC), PM_2.5, ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide were measured simultaneously each day. Fifteen homes in the Boston area were measured for 7 days during winter and summer. A previous paper explored the associations between personal-indoor, personal-outdoor, personal-ambient, indoor-outdoor, indoor-ambient and outdoor-ambient PM_2.5, sulfate and EC concentrations. For the current paper, factors that may affect personal exposures were investigated, while controlling for ambient concentrations. The data were analyzed using mixed effects regression models. Overall personal-ambient associations were strong for sulfate during winter (p < 0.0001) and summer (p < 0.0001) and PM_2.5 during summer (p < 0.0001). The personal-ambient mixed model slope for PM_2.5 during winter but was not significant at p = 0.10. Personal exposures to most pollutants, with the exception of NO₂, increased with ventilation and time spent outdoors. An opposite pattern was found for NO₂ likely due to gas stoves. Personal exposures to PM_2.5 and to traffic-related pollutants, EC and NO₂, were higher for those individuals living close to a major road. Both personal and indoor sulfate and PM_2.5 concentrations were higher for homes using humidifiers. The impact of outdoor sources on personal and indoor concentrations increased with ventilation, whereas an opposite effect was observed for the impact of indoor sources.     

Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta

High levels of PM_2.5 exposure and associated health risks are of great concern in rural China. For this study, we used portable PM_2.5 monitors for monitoring concentrations online, recorded personal time‐activity patterns, and analyzed the contribution from different microenvironments in rural areas of the Yangtze River Delta, China. The daily exposure levels of rural participants were 66 μg/m³ (SD 40) in winter and 65 μg/m³ (SD 16) in summer. Indoor exposure levels were usually higher than outdoor levels. The exposure levels during cooking in rural kitchens were 140 μg/m³ (SD 116) in winter and 121 μg/m³ (SD 70) in summer, the highest in all microenvironments. Winter and summer values were 252 μg/m³ (SD 103) and 204 μg/m³ (SD 105), respectively, for rural people using biomass for fuel, much higher than those for rural people using LPG and electricity. By combining PM_2.5concentrations and time spent in different microenvironments, we found that 92% (winter) and 85% (summer) of personal exposure to PM_2.5in rural areas was attributable to indoor microenvironments, of which kitchens accounted for 24% and 27%, respectively. Consequently, more effective policies and measures are needed to replace biomass fuel with LPG or electricity, which would benefit the health of the rural population in China.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

Measured performance of filtration and ventilation systems for fine and ultrafine particles and ozone in an unoccupied modern California house

This study evaluated nine ventilation and filtration systems in an unoccupied 2006 house located 250 m downwind of the I‐80 freeway in Sacramento, California. Systems were evaluated for reducing indoor concentrations of outdoor particles in summer and fall/winter, ozone in summer, and particles from stir‐fry cooking. Air exchange rate was measured continuously. Energy use was estimated for year‐round operation in California. Exhaust ventilation without enhanced filtration provided indoor PM_2.5 that was 70% lower than outdoors. Supply ventilation with MERV13 filtration provided slightly less protection, whereas supply MERV16 filtration reduced PM_2.5 by 97‐98% relative to outdoors. Supply filtration systems used little energy but provided no benefits for indoor‐generated particles. Systems with MERV13‐16 filter in the recirculating heating and cooling unit (FAU) operating continuously or 20 min/h reduced PM_2.5 by 93‐98%. Across all systems, removal percentages were higher for ultrafine particles and lower for black carbon, relative to PM_2.5. Indoor ozone was 3‐4% of outdoors for all systems except an electronic air cleaner that produced ozone. Filtration via the FAU or portable filtration units lowered PM_2.5 by 25‐75% when operated over the hour following cooking. The energy for year‐round operation of FAU filtration with an efficient blower motor was estimated at 600 kWh/year.     

Seasonal variability in environmental tobacco smoke exposure in public housing developments

The risk of tobacco smoking and second‐hand smoke (SHS) exposure combined are the leading contributors to disease burden in high‐income countries. Recent studies and policies are focusing on reducing exposure to SHS in multiunit housing (MUH), especially public housing. We examined seasonal patterns of SHS levels within indoor common areas located on Boston Housing Authority (BHA) properties. We measured weekly integrated and continuous fine particulate matter (PM_2.5) and passive airborne nicotine in six buildings of varying building and occupant characteristics in summer 2012 and winter 2013. The average weekly indoor PM_2.5 concentration across all six developments was 9.2 μg/m³, higher during winter monitoring period (10.3 μg/m³) compared with summer (8.0 μg/m³). Airborne nicotine concentrations ranged from no detection to about 5000 ng/m³ (mean 311 ng/m³). Nicotine levels were significantly higher in the winter compared with summer (620 vs. 85 ng/m³; 95% CI: 72–998). Smoking‐related exposures within Boston public housing vary by season, building types, and resident smoking policy. Our results represent exposure disparities that may contribute to health disparities in low‐income communities and highlight the potential importance of efforts to mitigate SHS exposures during winter when outdoor–indoor exchange rates are low and smokers may tend to stay indoors. Our findings support the use of smoke‐free policy as an effective tool to eliminate SHS exposure and protect non‐smokers, especially residents of MUH.     

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community

Indoor and outdoor concentrations of PM_2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM_2.5, data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM_2.5 were significantly higher during the heating season (indoor: 36.2 μg/m³; outdoor: 22.1 μg/m³) compared with the non-heating season (indoor: 14.6 μg/m³; outdoor: 9.3 μg/m³). Heating season indoor PM_2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM_2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM_2.5). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM_2.5 concentrations that exceeded both the EPA ambient standard (35 μg/m³) and the WHO guideline (25 μg/m³).     

Concurrent assessment of personal,indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices

The intensity, frequency, duration, and contribution of distinct PM_2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM_2.5 and PM₁ monitoring using novel low-cost sensing (LCS) devices, AS-LUNG. GRIMM-comparable observations were acquired by the corrected AS-LUNG readings, with R² up to 0.998. Twenty-six non-smoking healthy adults were recruited in Taiwan in 2018 for 7-day personal, home indoor, and home outdoor PM monitoring. The results showed 5-min PM_2.5 and PM₁ exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m³, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM_2.5 increments of 76.5 and 183.8 µg/m³ (1 min), respectively. Incense burning had the highest mean PM_2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM_2.5 increments (15.0 µg/m³) to indoor levels, among all single sources. Certain events accounted for 14.0%-39.6% of subjects’ daily exposures. With the high resolution of AS-LUNG data and detailed time-activity diaries, the impacts of sources and ventilations were assessed in detail.     

Particle characterization in retail environments: concentrations,sources, and removal mechanisms

Particles in retail environments can have consequences for the occupational exposures of retail workers and customers, as well as the energy costs associated with ventilation and filtration. Little is known about particle characteristics in retail environments. We measured indoor and outdoor mass concentrations of PM₁₀ and PM_2.5, number concentrations of submicron particles (0.02–1 μm), size‐resolved 0.3–10 μm particles, as well as ventilation rates in 14 retail stores during 24 site visits in Pennsylvania and Texas. Overall, the results were generally suggestive of relatively clean environments when compared to investigations of other building types and ambient/occupational regulatory limits. PM₁₀ and PM_2.5 concentrations (mean ± s.d.) were 20 ± 14 and 11 ± 10 μg/m³, respectively, with indoor‐to‐outdoor ratios of 1.0 ± 0.7 and 0.88 ± 1.0. Mean submicron particle concentrations were 7220 ± 7500 particles/cm³ with an indoor‐to‐outdoor ratio of 1.18 ± 1.30. The median contribution to PM₁₀ and PM_2.5 concentrations from indoor sources (vs. outdoors) was 83% and 53%, respectively. There were no significant correlations between measured ventilation rates and particle concentrations of any size. When examining options to lower PM_2.5 concentrations below regulatory limits, the required changes to ventilation and filtration efficiency were site specific and depended on the indoor and outdoor concentration, emission rate, and infiltration level.     

Wood stove use and other determinants of personal and indoor exposures to particulate air pollution and ozone among elderly persons in a Northern Suburb

A six‐month winter‐spring study was conducted in a suburb of the northern European city of Kuopio, Finland, to identify and quantify factors determining daily personal exposure and home indoor levels of fine particulate matter (PM_2.5, diameter <2.5 µm) and its light absorption coefficient (PM_2.5abs), a proxy for combustion‐derived black carbon. Moreover, determinants of home indoor ozone (O₃) concentration were examined. Local central site outdoor, home indoor, and personal daily levels of pollutants were monitored in this suburb among 37 elderly residents. Outdoor concentrations of the pollutants were significant determinants of their levels in home indoor air and personal exposures. Natural ventilation in the detached and row houses increased personal exposure to PM_2.5, but not to PM_2.5abs, when compared with mechanical ventilation. Only cooking out of the recorded household activities increased indoor PM_2.5. The use of a wood stove room heater or wood‐fired sauna stove was associated with elevated concentrations of personal PM_2.5 and PM_2.5abs, and indoor PM_2.5abs. Candle burning increased daily indoor and personal PM_2.5abs, and it was also a determinant of indoor ozone level. In conclusion, relatively short‐lasting wood and candle burning of a few hours increased residents’ daily exposure to potentially hazardous, combustion‐derived carbonaceous particulate matter.     

Variation in indoor particle number and PM2.5 concentrations in a radio station surrounded by busy roads before and after an upgrade of the HVAC system

Indoor particle number and PM_2.5 concentrations were investigated in a radio station surrounded by busy roads. Two extensive field measurement campaigns were conducted to determine the critical parameters affecting indoor air quality. The results indicated that indoor particle number and PM_2.5 concentrations were governed by outdoor air, and were significantly affected by the location of air intake and design of HVAC system. Prior to the upgrade of the HVAC system and relocation of the air intake, the indoor median particle number concentration was 7.4×10³ particles/cm³ and the median PM_2.5 concentration was 7 μg/m³. After the relocation of air intake and the redesign of the HVAC system, the indoor particle number concentration was between 2.3×10³ and 3.4×10³ particles/cm³, with a median value of 2.7×10³ particles/cm³, and the indoor PM_2.5 concentration was in the range of 3–5 μg/m³, with a median value of 4 μg/m³. By relocating the air intake of the HVAC, the outdoor particle number and PM_2.5 concentrations near the air intake were reduced by 35% and 55%, respectively. In addition, with the relocation of air intake and the redesign of the HVAC system, the particle number penetration rate was reduced from 42% to 14%, and the overall filtration efficiency of the HVAC system (relocation of air intake, pre-filter, AHU and particle losses in the air duct) increased from 58% to 86%. For PM_2.5, the penetration rate after the upgrade was approximately 18% and the overall filtration efficiency was 82%. This study demonstrates that by using a comprehensive approach, including the assessment of outdoor conditions and characterisation of ventilation and filtration parameters, satisfactory indoor air quality can be achieved, even for those indoor environments facing challenging outdoor air conditions.     

Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China

Atmospheric particulate matter (PM_2.5, PM₁₀ and TSP) were sampled synchronously during three monitoring campaigns from June 2007 to February 2008 at a coastal site in TEDA of Tianjin, China. Chemical compositions including 19 elements, 6 water-solubility ions, organic and elemental carbon were determined. principle components analysis (PCA) and chemical mass balance modeling (CMB) were applied to determine the PM sources and their contributions with the assistance of NSS SO₄²⁻, the mass ratios of NO₃⁻ to SO₄²⁻ and OC to EC. Air mass backward trajectory model was compared with source apportionment results to evaluate the origin of PM. Results showed that NSS SO₄²⁻ values for PM_2.5 were 2147.38, 1701.26 and 239.80 ng/m³ in summer, autumn and winter, reflecting the influence of sources from local emissions. Most of it was below zero in summer for PM₁₀ indicating the influence of sea salt. The ratios of NO₃⁻ to SO₄²⁻ was 0.19 for PM_2.5, 0.18 for PM₁₀ and 0.19 for TSP in winter indicating high amounts of coal consumed for heating purpose. Higher OC/EC values (mostly larger than 2.5) demonstrated that secondary organic aerosol was abundant at this site. The major sources were construction activities, road dust, vehicle emissions, marine aerosol, metal manufacturing, secondary sulfate aerosols, soil dust, biomass burning, some pharmaceutics industries and fuel-oil combustion according to PCA. Coal combustion, marine aerosol, vehicular emission and soil dust explained 5-31%, 1-13%, 13-44% and 3-46% for PM_2.5, PM₁₀ and TSP, respectively. Backward trajectory analysis showed air parcels originating from sea accounted for 39% in summer, while in autumn and winter the air parcels were mainly related to continental origin.     

Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools

School-age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real-time PM₁₀, PM_2.5, NO_2, and O₃ concentrations were measured in 76 classrooms and 23 non-classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM_2.5, PM₁₀, O_3, and NO₂ microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM_2.5, PM₁₀, O_3, and NO₂ microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.     

Indoor and outdoor PM2.5 and PM10 concentrations in the air during a dust storm

Asian dust storms (ADS) originating from the arid deserts of Mongolia and China are a well-known springtime meteorological phenomenon throughout East Asia. The ventilation systems in office utilize air from outside and therefore it is necessary to understand how these dust storms affect the concentrations of PM_2.5 and PM₁₀ in both the indoor and outdoor air. We measured dust storm pollution particles in an office building using a direct-reading instrument (PC-2 Quartz Crystal Microbalance, QCM) that measured particle size and concentration every 10 min for 1 h, three times a day. A three-fold increase in the concentrations of PM_2.5 and PM₁₀ in the indoor and outdoor air was recorded during the dust storms. After adjusting for other covariates, autoregression models indicated that PM_2.5 and PM₁₀ in the indoor air increased significantly (21.7 μg/m³ and 23.0 μg/m³ respectively) during dust storms. The ventilation systems in high-rise buildings utilize air from outside and therefore the indoor concentrations of fine and coarse particles in the air inside the buildings are significantly affected by outside air pollutants, especially during dust storms.     

Infiltration of fine particles in urban daycares

Singapore is a tropical country with a high density of day-care facilities whose indoor environments may be adversely affected by outdoor fine particle (PM_2.5) air pollution. To reduce this problem requires effective, evidence-based exposure-reduction strategies. Little information is available on the penetration of outdoor PM_2.5 into day-care environments. Our study attempted to address the following objectives: to measure indoor infiltration factor (F_inf) of PM_2.5 from outdoor PM_2.5 and to determine the building parameters that modify the indoor PM_2.5. We collected indoor/outdoor 1-min PM_2.5 from 50 day-care classrooms. We noted mean F_inf ± SD of 0.65 ± 0.22 in day-care rooms which are naturally ventilated and lower F_inf ± SD values of 0.47 ± 0.18 for those that are air-conditioned: values which are lower than those reported in Singapore residences. The air exchange rates were higher in naturally ventilated rooms (1.47 vs 0.86 h⁻¹). However, fine particle deposition rates were lower for naturally ventilated rooms (0.67 ± 0.43 h⁻¹) compared with air-conditioned ones (1.03 ± 0.55 h⁻¹) presumably due to composite rates linked to the filters within the split unit air-conditioners, higher recirculation rates, and interior surfaces in the latter. Our findings indicate that children remaining indoor in daycares where air-conditioning is used can reduce their PM_2.5 exposures during outdoor pollution episodes.     

Particulate reactive oxygen species on total suspended particles – measurements in residences in Austin,Texas

The biologically relevant characteristics of particulate matter (PM) in homes are important to assessing human health. The concentration of particulate reactive oxygen species (ROS) was assessed in eight homes and was found to be lower inside (mean ± s.e. = 1.59 ± 0.33 nmol/m³) than outside (2.35 ± 0.57 nmol/m³). Indoor particulate ROS concentrations were substantial and a major fraction of indoor particulate ROS existed on PM_2.5 (58 ± 10%), which is important from a health perspective as PM_2.5 can carry ROS deep into the lungs. No obvious relationships were evident between selected building characteristics and indoor particulate ROS concentrations, but this observation would need to be verified by larger, controlled studies. Controlled experiments conducted at a test house suggest that indoor ozone and terpene concentrations substantially influence indoor particulate ROS concentrations when outdoor ozone concentrations are low, but have a weaker influence on indoor particulate ROS concentrations when outdoor ozone concentrations are high. The combination of substantial indoor concentrations and the time spent indoors suggest that further work is warranted to assess the key parameters that drive indoor particulate ROS concentrations.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

Different health effects of indoor‐ and outdoor‐originated PM2.5 on cardiopulmonary function in COPD patients and healthy elderly adults

Numerous research has explored the associations of outdoor or indoor fine particulate matter (PM_2.5) and health effects; however, few studies compared the effects of indoor PM_2.5 originated from outdoor (PM_2.5,os) and indoor sources (PM_2.5,is). To assess the associations of PM_2.5,os and PM_2.5,is with cardiopulmonary function in patients with chronic obstructive pulmonary disease (COPD) and healthy elderly adults, blood pressure (BP) and pulmonary function were repeatedly examined in 43 COPD patients and their 32 healthy spouses in Beijing, China. Iron was used as tracer element to separate PM_2.5,os and PM_2.5,is. Mixed‐effects models were applied to assess the associations of PM_2.5,os or PM_2.5,is and health effects after controlling for potential confounders. There was a reduction in forced expiratory volume in first second (FEV₁) in COPD patients associated with PM_2.5,is during the heating season. PM_2.5,os was positively associated with diastolic BP (DBP) in healthy elderly adults during the heating season. There was a reduction in peak expiratory flow (PEF) in healthy elderly adults associated with PM_2.5,os during the non‐heating season. Exposure to indoor‐ and outdoor‐originated PM_2.5 had different health effects on cardiopulmonary function in different populations. The results provide supporting evidence for improving indoor air quality to promote public health among susceptible population.     

Cooking‐related PM2.5 and acrolein measured in grocery stores and comparison with other retail types

We measured particulate matter (PM), acrolein, and other indoor air contaminants in eight visits to grocery stores in California. Retail stores of other types (hardware, furniture, and apparel) were also sampled on additional visits. Based on tracer gas decay data, most stores had adequate ventilation according to minimum ventilation rate standards. Grocery stores had significantly higher concentrations of acrolein, fine and ultrafine PM, compared to other retail stores, likely attributable to cooking. Indoor concentrations of PM_2.5 and acrolein exceeded health guidelines in all tested grocery stores. Acrolein emission rates to indoors in grocery stores had a mean estimate about 30 times higher than in other retail store types. About 80% of the indoor PM_2.5 measured in grocery stores was emitted indoors, compared to only 20% for the other retail store types. Calculations suggest a substantial increase in outdoor air ventilation rate by a factor of three from current level is needed to reduce indoor acrolein concentrations. Alternatively, acrolein emission to indoors needs to be reduced 70% by better capturing of cooking exhaust. To maintain indoor PM_2.5 below the California annual ambient standard of 12 μg/m³, grocery stores need to use air filters with an efficiency rating higher than the MERV 8 air filters commonly used today.     

Polycyclic aromatic hydrocarbons: levels and phase distributions in preschool microenvironment

This work aims to characterize levels and phase distribution of polycyclic aromatic hydrocarbons (PAHs) in indoor air of preschool environment and to assess the impact of outdoor PAH emissions to indoor environment. Gaseous and particulate (PM₁ and PM_2.5) PAHs (16 USEPA priority pollutants, plus dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were concurrently sampled indoors and outdoors in one urban preschool located in north of Portugal for 35 days. The total concentration of 18 PAHs (ΣPAHs) in indoor air ranged from 19.5 to 82.0 ng/m³; gaseous compounds (range of 14.1–66.1 ng/m³) accounted for 85% ΣPAHs. Particulate PAHs (range 0.7–15.9 ng/m³) were predominantly associated with PM₁ (76% particulate ΣPAHs) with 5‐ring PAHs being the most abundant. Mean indoor/outdoor ratios (I/O) of individual PAHs indicated that outdoor emissions significantly contributed to PAH indoors; emissions from motor vehicles and fuel burning were the major sources.