Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

Cumulative data indicate that pollen grains and air pollution reciprocally interact. Climate changes seem also to influence pollen allergenicity. Depending on the plant species and on the pollutant type and concentration, this interaction may modify the features and metabolism of the pollen grain. Previous results revealed a significant positive correlation between pollen and aeroallergen, even using two different samplers. However, some discrepancy days have been also detected with low pollen but high aeroallergen concentrations. The main aim of the present paper is to find how the environmental factors, and specially pollutants, could affect the amount of allergens from olive and grass airborne pollen. Pollen grains were collected by a Hirst-type volumetric spore trap. Aeroallergen was simultaneously sampled by a low-volume Cyclone Burkard sampler. Phl p 5 and Ole e 1 aeroallergen were quantified by double-sandwich ELISA test. The data related to air pollutants, pollen grains, and aeroallergens were analyzed with descriptive statistic. Spearman’s correlation test was used to identify potential correlations between these variables. There is a significant positive correlation between aeroallergens and airborne pollen concentrations, in both studied pollen types, so allergen concentrations could be explained with the pollen concentration. The days with unlinked events coincide between olive and grass allergens. Nevertheless, concerning to our results, pollutants do not affect the amount of allergens per pollen. Even if diverse pollutants show an unclear relationship with the allergen concentration, this association seems to be a casual effect of the leading role of some meteorological parameters.