Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Recently, there has been a substantial increase in the availability and use of low-cost particulate matter sensors in a wide range of air quality applications. They carry the promise of revolutionising air quality monitoring, yet considerable reservations exist regarding their performance and capabilities, thus hindering the broader-scale utilization of these devices. In order to address these concerns and assess their feasibility and accuracy for various applications, we evaluated six low-cost PM 2.5 sensors (the Sharp GP2Y1010AU0F, Shinyei PPD42NS, Plantower PMS1003, Innociple PSM305, Nova SDS011 and Nova SDL607) in laboratory and field conditions using two combustion aerosols, concrete dust and ambient particles. In assessing the performance of these sensors, we focussed on indicators such as the linearity, accuracy and precision, critically differentiating between these qualities, and employed inter-comparison (the coefficient of determination, R 2 ). In the laboratory, all sensors responded well, with an R 2 > 0.91 when the PM 2.5 concentration was > 50 µg m -3 , as measured by the DustTrak. In particular, the PMS1003 demonstrated good accuracy and precision in both laboratory and ambient conditions. However, some limitations were noted for the tested sensors at lower concentrations. For example, the Sharp and Shinyei sensors showed poor correlations (R 2 < 0.1) with the DustTrak when the ambient PM 2.5 concentration was < 20 µg m -3 . These results suggest that the sensors should be calibrated individually for each source in the environment of their intended use. We demonstrate that when tested appropriately and used with a full understanding of their capabilities and limitations, low-cost sensors can serve as an unprecedented aid in a broad spectrum of air quality applications, including the emerging field of citizen science.

No abstract

No abstract

This study was undertaken to investigate the mineral profile of pito, a traditionally brewed alcoholic beverage popularly consumed along West Coast of Africa. Pito samples from four cities in Ghana namely; Bolgatanga, Tamale, Wa and Accra were analysed for their metal content. Concentrations of Na, K, Mn, Cu and Zn, were measured in all the samples analysed. However, Fe, Ni, Cd and Pb recorded 70%, 83%, 58% and 79% incidence respectively in the samples, but Cr was measured below detection limit in all the samples. The concentrations of Na, K, Mn, Cu, Zn, Fe, Ni, Pb and Cd recorded ranged between 15 to 66mg/L, 581 to 1108mg/L, 0.152 to 0.808mg/L, 0.076 to 0.308mg/L, 0.456 to 0.910mg/L, 0.308 to 2.832mg/L, 0.040 to 0.176mg/L, 0.056 to 0.272mg/L and 0.011 to 0.048mg/L respectively. With the exception of Mn, all the essential minerals measured were below the recommended WHO maximum limits in water. Hence pito is a good source of K, Na, Fe, Cu and Zn. The detected concentrations of Ni, Pb and Cd in the pito samples were however, above the respective maximum WHO guideline in water. Therefore pito is susceptible to metal contamination due to poor handling and primitive equipment used in the production and consumers should be apprehensive of the environs where the pito is prepared.

The study aimed to identify diurnal indoor temperature patterns and quantify the impact of outdoor on indoor temperature as well as of other modifying factors. Indoor and outdoor temperatures of 77 houses in Brisbane, Australia were monitored with temperature sensors for one year (May 2017(May -2018. A linear mixed effect model predicted that on average, a 1 o C increase in outdoor temperature resulted in a 0.41 o C increase in indoor temperature during both the cool and warm seasons. The age of the house, building material, roof material and insulation had a moderate influence on indoor temperature. Queenslander houses (a stand-alone timber structure mounted on stumps with an extensive veranda) were, in general, cooler (0.5 o C cooler in winter) and reactive (meaning, having a strong association with the outdoor temperature), while slab-on-ground houses were, in general, warmer (0.3 o C) and stable (meaning, having less association with the outdoor temperature). From the indoor temperature patterns identified for the heated and cooled houses it was concluded that in this climate, heating and cooling is seldom done for 24 hours. This quantitative information is crucial for understanding the influence of temperature on human health and household energy consumption at the time when climate change mitigation approaches are being discussed.

No abstract

Epidemiological studies on the impact of outdoor temperature to human health have demonstrated the capability of humans to adapt to local climate. However, there is limited information on the association between indoor temperature and human health, despite people spending most of their time indoors. The problem stems from the lack of sufficient indoor temperature measurement in the population. To overcome this obstacle, this paper presents an indirect epidemiological approach to evaluate the impact of high indoor temperature on mortality. The relationships between indooroutdoor temperatures in different climate zones identified in the literature, were combined with the outdoor temperature-mortality curves of the same locations to obtain the local indoor minimum mortality temperatures (iMMT), the temperature at which mortality is lowest, which by implication is the temperature at which the population is most comfortable on average . We show that the iMMT varies and has a weak linear relationship with the distance to the equator, which provides evidence of human adaptation to local indoor temperatures. These findings reinforce the adaptive comfort theory, which states that people can adapt to local indoor environment and establish their thermal comfort. Recognising the human adaptability to local climate will direct flexible and optimized policy to protect public health against extreme temperature events. This will also help reduce energy consumption for regulating indoor temperature without compromising the occupants' health.