## 2 Current and Future Challenges in Air Quality Monitoring
[6] investigated the concentration level of PM2.5 and PM10 as well as their carbonaceous fraction, including organic carbon (OC), elemental carbon (EC) and total carbon (TC) from samples collected from five different sectors in Pakistan. It is well-known that studying the chemical composition of particulate matter (PM) provides an opportunity to conduct additional studies on source identification, impact assessment and trend analysis. Furthermore, [PERSON] et al. [7] have shown that quantitative assessments of chemical and biological properties of ambient PM2.5 and VOCs can be used effectively to characterize, compare and contrast air pollution across different geographical regions (Los Angeles basin) to account for effects of atmospheric modifications on air mass and to evaluate exposure proximity to an emission source. Observational data from city AQ monitoring stations are usually analyzed and used for numerical simulations in order to evaluate the impact of emission control scenarios. To that end, [PERSON] et al. [8] analyzed the air quality observational data of major air pollutants in 2015 and during pollution episodes in Haizhu district, China, and the impacts of emission control scenarios on air quality by the year 2020 were evaluated using a WRF-Chem numerical simulation. In the same direction, [PERSON] et al. [9] investigated the pollution characteristics, transport pathways and potential sources of PM2.5 based on PM2.5 monitoring data from 2015 to 2016 in Weifang, China. For that purpose, they used three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF) and concentration weighted trajectory (CWT). Nowadays, fine dust data acquired by various personal monitoring devices is of great value as training data for predicting future fine dust concentrations and innovatively alerting people of potential danger. However, most of the fine dust data obtained from these devices include either missing or abnormal data caused by various factors such as sensor malfunction, transmission errors or storage errors. [PERSON] et al. [10] presented methods for interpolating the missing data and detecting anomalies in PM2.5 time series data. These methods are expected to contribute greatly to improving the reliability of data. Air pollution data obtained from various monitoring campaigns are usually used for health impact assessment. [PERSON] et al. [11] examined the concentrations and sources of PAHs and trace metals in indoor dust and, more specifically, focused on residences with infants and young children. Exposure to toxicants contained in household dust is of paramount concern, especially in the case of young children, due to their particular behavioral characteristics. In this context, extracts of sieved vacuum cleaner dust from 20 residences with young children in Athens, Greece were examined for the presence of PAHs and trace metals. Outdoor environment and, more specifically, industrialized areas also play a significant role in citizens' health. To that end, [PERSON] et al. [12] simultaneously monitored PAHs, PCDD/Fs, dIPCBs and indPCBs bonded to particulate matter (PM10) for the estimation of their health risks to nearby citizens. SPSS statistical package was employed for statistical analysis and source apportionment purposes. Cancer risk was also estimated from total persistent organic pollutants (POPs) dataset according to the available literature. Specific attention should be given to studies on the health impacts of air pollution where regression analysis is used. The complexity of count data regression models can lead to false inference and overfitting. [PERSON] et al. (2013) presented a simple histogram of predicted and observed count values (POCH) which, while rarely found in the environmental literature but presented in authoritative statistical texts, can dramatically reduce the risk of accepting untrue hypotheses.