Medium-term Air Quality Benchmarking for Ecosystem Monitoring and Sustainability Planning: Case Study Dallas County (U.S.A.) 2015 to 2020

Medium-term air quality assessment, benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes. By using daily and monthly averaged data, medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives. By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark (CLAB). The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator. Such a benchmark can provide an overall air quality assessment for areas of interest. A case study is presented for Dallas County (U.S.A.) applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019. Six air pollutants considered are: ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, benzene and particulate matter less than 2.5 micrometres. These pollutants are assessed individually and in terms of CLAB, and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019. Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years. The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs. Conducting CLAB analysis on an ongoing basis, using a relevant nearpast time interval for benchmarking that covers several years, can reveal useful monthly, seasonal and annual trends in overall air quality. This type of medium-term, benchmarked air quality data analysis is well suited for ecosystem monitoring.

Microscopic Fungi in Big Cities: Biodiversity, Source, and Relation to Pollution by Potentially Toxic Metals

For the first time, a mycological analysis of outdoor urban environment (air, leaves, sealed surfaces) was carried in the cities of subarctic (Murmansk) and temperate (Moscow) climatic zones. The chemical composition of dust deposited on leaves of dominant tree species was taken as an indicator of the air quality. Assessment of the complex impact of factors (climate zone, type of substrate, anthropogenic load) on the quantitative and qualitative parameters of mycobiome was performed. Compared to Moscow, Murmansk was characterized by an increased number and concentrations of pollutants in the deposited dust. The number of culturable airborne fungi in Murmansk was substantially lower than in Moscow. Half of the species belonged to the opportunistic in both cities. Most dangerous opportunistic fungi were absent in the air of recreational zones but present on leaves surface and in road dust in all assessed zones of the cities. Dust chemical composition affected the diversity of fungi species. While the relationship of biological parameters with concentration of potentially toxic metals was generally negative, Cd increased the fraction of opportunistic fungi in road dust. The study revealed an importance of substrate in determining the sensitivity of outdoor mycobiome to pollution and highlighted its biological characteristics sensitive to climate.

Epiphytic Cryptogams as Bioindicators of Air Quality in a Tropical Andean City

Air pollution is one of the main environmental problems in developed and developing countries. Epiphytic cryptogams (bryophytes and lichens) are proposed as a reliable indicator to detect environmental changes, given to their sensitivity to pollutants. In this study we evaluated air quality in the city of Ambato using bryophytes and epiphytic lichens on three land uses (urban, peri-urban and control). In each zone we selected ten trees (a total of 90 trees) for each station (a total of nine stations), where we recorded the frequency and cover of epiphytic cryptogams in a quadrat of 10 × 50 cm that was divided into 5 × 5 cm squares. Differences in richness, index of atmospheric purity (IAP) and diversity were analyzed using a generalized linear model (GLM) and changes in species composition using multivariate analysis. We recorded 39 species of cryptogams (25 lichens and 14 bryophytes). Richness, diversity and index of atmospheric purity were higher in the control zone compared to the urbanized zones. Community composition changed between the different zones, with increasing differences between the control and urban zones. The urban areas of the city of Ambato were identified with high levels of air pollution due to their lower diversity related to higher vehicular traffic and industrial activities (e.g., footwear and textile factories, tanneries). Thus, epiphytic cryptogams are a fast and low-cost method for air quality assessment in tropical areas.

Air Quality Assessment of a School in an Industrialized Area of Southern Italy

In this study, simultaneous monitoring of indoor and outdoor Volatile Organic Compounds (VOCs) was carried out in a school, by both Radiello® cartridges and real-time monitors (Corvus, IonScience Ltd., Fowlmere UK). Moreover, an outdoor air quality assessment was performed with data from an air quality monitoring station (ARPA Puglia) located close to the school. In particular, VOCs, Benzene, Toluene, Ethylbenzene, and Xilenes concentrations, obtained by using Radiello® diffusive samplers, were monitored in two classrooms, two bathrooms, and outside of the school building for three weeks during winter 2019. Simultaneously, the Total VOC (TVOC) concentrations were measured by means of real-time monitors inside and outside the classroom in order to individuate the activation of sources during the sampling campaign days. The results evidence that indoor TVOC concentrations were bigger than those outdoors; this suggests the presence of indoor pollutant sources, both in the classrooms and the bathrooms. The results of our study can help the school management by the following recommendations: increasing ventilation in classrooms during school activities and after cleaning; involving students in discussions regarding the use of acrylic paints, permanent markers, perfumes and deodorants, and the limitation of smoking activities both in bathrooms and outdoor spaces.

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.