Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors.

Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM2.5 and PM10 (3.9 ± 6.8 ug/m3 and 9.6 ± 12.7 µg/m3 respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m3 and 4.2 ± 6.9 µg/m3 respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM1 concentration (1.6 ± 1.9 µg/m3), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM1 was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM2.5, and up to 50 % of the PM10, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.

Constructing a pollen proxy from low-cost Optical Particle Counter (OPC) data processed with Neural Networks and Random Forests.

Pollen allergies affect a significant proportion of the global population, and this is expected to worsen in years to come. There is demand for the development of automated pollen monitoring systems. Low-cost Optical Particle Counters (OPCs) measure particulate matter and have attractive advantages of real-time high time resolution data and affordable costs. This study asks whether low-cost OPC sensors can be used for meaningful monitoring of airborne pollen. We employ a variety of methods, including supervised machine learning techniques, to construct pollen proxies from hourly-average OPC data and evaluate their performance, holding out 40 % of observations to test the proxies. The most successful methods are supervised machine learning Neural Network (NN) and Random Forest (RF) methods, trained from pollen concentrations collected from a Hirst-type sampler. These perform significantly better than using a simple particle size-filtered proxy or a Positive Matrix Factorisation (PMF) source apportionment pollen proxy. Twelve NN and RF models were developed to construct a pollen proxy, each varying by model type, input features and target variable. The results show that such models can construct useful information on pollen from OPC data. The best metrics achieved (Spearman correlation coefficient = 0.85, coefficient of determination = 0.67) were for the NN model constructing a Poaceae (grass) pollen proxy, based on particle size information, temperature, and relative humidity. Ability to distinguish high pollen events was evaluated using F1 Scores, a score reflecting the fraction of true positives with respect to false positives and false negatives, with promising results (F1 ≤ 0.83). Model-constructed proxies demonstrated the ability to follow monthly and diurnal trends in pollen. We discuss the suitability of OPCs for monitoring pollen and offer advice for future progress. We demonstrate an attractive alternative for automated pollen monitoring that could provide valuable and timely information to the benefit of pollen allergy sufferers.

Health risk associated with airborne particulate matter and its components in Jeddah, Saudi Arabia.

Samples of PM2.5 and PM10 have been collected in all of four seasons at seven sites within the city of Jeddah, Saudi Arabia. The samples have been analysed for a range of trace elements. There is a large loading of wind-blown dust and the majority of elements are predominantly associated with coarse particles. Enrichment factors, however, show that some elements are markedly enriched above crustal abundance. Using mean data for the PM2.5 and PM10 fractions from each of the seven sampling sites, health risks have been estimated for particulate matter mass, the elements Cr, Mn, Ni, Pb, As, Cd and V measured in this study, and polycyclic aromatic hydrocarbons using data from an earlier study within Jeddah. Cancer risks are calculated from mean airborne concentrations and cancer slope factors for the carcinogenic metals and PAH, but the cancer risks are relatively modest compared to the lifetime risk of mortality due to PM2.5 exposure. The risks associated with exposure to V and Mn are considered to be small, while concentrations of cadmium far exceed the European Union Limit Value and World Health Organisation guideline. Cadmium shows a very high crustal enrichment factor but is present predominantly in the coarse particle fraction suggesting that local soils and surface dusts are unusually enriched in Cd relative to the global average. Using national data for mortality rates, the excess mortality due to PM2.5 exposure has been calculated and amounts to over 1100 deaths annually for the city of Jeddah.