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Abstract Exhaled SARS-CoV-2-containing aerosols contributed significantly to the rapid and vast spread of covid-19. However, quantitative experimental data on the infectivity of such aerosols is missing. Here, we quantified emission rates of infectious viruses in exhaled aerosol from individuals within their first days after symptom onset from covid-19. Six aerosol samples from three individuals were culturable, of which five were successfully quantified using TCID50. The source strength of the three individuals was highest during singing, when they exhaled 4, 36, or 127 TCID50/s, respectively. Calculations with an indoor air transmission model showed that if an infected individual with this emission rate entered a room, a susceptible person would inhale an infectious dose within 6 to 37 min in a room with normal ventilation. Thus, our data show that exhaled aerosols from a single person can transmit covid-19 to others within minutes at normal indoor conditions.

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73–0.85) and nitrogen dioxide (NO2, r = 0.68–0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80–0.86; mean absolute error MAE = 3.90–4.73 %) and at the urban background site in Dresden (R2 = 0.79–0.84; MAE = 4.23–4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Background: Suspected aerosol-generating dental instruments may cause risks for operators by transmitting pathogens, such as the SARS-CoV-2 virus. The aim of our study was to measure aerosol generation in various dental procedures in clinical settings. Methods: The study population comprised of 84 patients who underwent 253 different dental procedures measured with Optical Particle Sizer in a dental office setting. Aerosol particles from 0.3 to 10 μm in diameter were measured. Dental procedures included oral examinations (N = 52), restorative procedures with air turbine handpiece (N = 8), high-speed (N = 6) and low-speed (N = 30) handpieces, ultrasonic scaling (N = 31), periodontal treatment using hand instruments (N = 60), endodontic treatment (N = 12), intraoral radiographs (N = 24), and dental local anesthesia (N = 31). Results: Air turbine handpieces significantly elevated 5 μm median (p = 0.023), maximum (p = 0.013) particle number concentrations,> 5 μm particle mass concentrations (p = 0.021) and maximum total particle mass concentrations (p = 0.022). High-speed dental handpieces elevated aerosol concentration levels compared to the levels produced during oral examination. Conclusions: Air turbine handpieces produced the highest levels of

Ultrafine particles (UFPs) usually explosive growth during new particle formation (NPF) events. However, the risk of exposure to UFPs on NPF days has been ignored due to the prevalence of mass-based air quality standards. In this study, the daily deposited doses, i.e., the daily deposited particle number dose (DPNd), mass dose (DPMd), and surface area dose (DPSd), of ambient particles in the human respiratory tract in Beijing were evaluated based on the particle number size distribution (3 nm-10 µm) from June 2018 to May 2019 utilizing a Multiple-Path Particle Dosimetry Model (MPPD) after the hygroscopic growth of particles in the respiratory tract had been accounted for. Our observations showed a high frequency (72.6%) of NPF on excellent air quality days, with daily mean PM2.5 concentrations less than 35 μg m−3. The daily DPNd on excellent air quality days was comparable with that on polluted days, although the DPMd on excellent air quality days was as low as 15.6% of that on polluted days. The DPNd on NPF days was ~1.3 times that on non-NPF days. The DPNd in respiratory tract regions decreased in the order: tracheobronchial (TB) > pulmonary (PUL) > extrathoracic (ET) on NPF days, while it was PUL > TB > ET on non-NPF days. The number of deposited nucleation mode particles, which were deposited mainly in the TB region (45%), was 2 times higher on NPF days than that on non-NPF days. Our results demonstrated that the deposition potential due to UFPs in terms of particle number concentrations is high in Beijing regardless of the aerosol mass concentration. More toxicological studies related to UFPs on NPF days, especially those targeting tracheobronchial and pulmonary impairment, are required in the future.

In this study, a combination of ground-based particulate matter measurements in synergy with space-borne CALIOP lidar recordings, meteorological observations, and reanalysis models have been used to study atmospheric air pollution over Amman, Jordan. The measurement was conducted over a 24-month period spanning from January 2018 to the end of December 2019. The CALIOP aerosol profiles and aerosol layer products version 4.21, level 2, with 5 km horizontal resolution were used to evaluate the vertical structure of the atmospheric constituent over the Amman region. The particle depolarization ratio (PDR) was extracted from CALIOP recordings and has been utilized to classify the type of atmospheric aerosols. This method reveals that the atmosphere above Amman mostly contains three different aerosol types including coarse-mode dust, fine-mode dust (polluted dust), and non-dust aerosols (pollution). Aerosols with 0 < δp≤ 0.075 are categorized as pollution, aerosols with 0.075 < δp≤ 0.20 as polluted dust, and aerosols with 0.20 < δp≤ 0.40 are classified as dust. Both the one- and two-step POlarization-LIdar PHOtometer Networking (POLIPHON) approaches have been applied to the CALIOP aerosol profile product to retrieve the vertical profile of the optical and micro-physical properties of each aerosol type. Lofted-layer top heights and layer thickness in the atmosphere above Amman during the study period were also extracted from the CALIOP aerosol layer products. The highest frequency of occurrence was observed for layers with a top height of 0.5 to 2.5 km with a second smaller peak at 3.5 km. The maximum frequency of the lofted layers (40% of cases) were observed with layer thickness below 0.5 km. For layers with a top height lower than 500 m above ground level, the atmosphere was mostly impacted by polluted dust and pollution aerosols. On the other hand, for layers with a top height above 2500 m agl, the atmosphere was contaminated by depolarizing dust particles.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality.As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

Poor air quality influences the quality of life in the urban environment. The regulatory observation stations provide the backbone for the city administration to monitor urban air quality. Recently a suite of cost-effective air quality sensors has emerged to provide novel insights into the spatio-temporal variability of aerosol particles and trace gases. Particularly in low concentrations these sensors might suffer from issues related e.g., to high detection limits, concentration drifts and interdependency between the observed trace gases and environmental parameters. In this study we characterize the optical particle detector used in AQT530 (Vaisala Ltd.) air quality sensor in the laboratory. We perform a measurement campaign with a network of AQT530 sensors in Helsinki, Finland in 2020–2021 and present a long-term performance evaluation of five sensors for particulate (PM10, PM2.5) and gaseous (NO2, NO, CO, O3) components during a half-year co-location study with reference instruments at an urban traffic site. Furthermore, short-term (3–5 weeks) co-location tests were performed for 25 sensors to provide sensor-specific correction equations for the fine-tuning of selected pollutants in the sensor network. We showcase the added value of the verified network of 25 sensor units to address the spatial variability of trace gases and aerosol mass concentrations in an urban environment. The analysis assesses road and harbor traffic monitoring, local construction dust monitoring, aerosol concentrations from fireworks, impact of sub-urban small scale wood combustion and detection of long-range transport episodes on a city scale. Our analysis illustrates that the calibrated network of Vaisala AQT530 air quality sensors provide new insights into the spatio-temporal variability of air pollution within the city. This information is beneficial to, for example, optimization of road dust and construction dust emission control as well as provides data to tackle air quality problems arising from traffic exhaust and localized wood combustion emissions in the residential areas.

Optimization of dry deposition velocity calculation has been of great interest. Every time, determining the value of the concentration boundary layer (CBL) thickness led to a waste of numerical calculation time, which appears as a huge time in large-scale climate models. The goal of this study is to optimize the numerical calculation time in the three-layer deposition model for smooth surfaces through the development of a MATLAB code that can parameterize the appropriate concentration boundary layer height (y+cbl) and internal integral calculation intervals for each particle diameter Dp (0.01–100 µm) and friction velocity u* (0.01–100 m/s). The particle concentration, as a solution to the particle flux equation, is obtained and modeled numerically by performing the left Riemann sum using MATLAB software. On the other hand, the number of subdivisions N of the Riemann sum was also parameterized for each Dp and u* in order to lessen the numerical calculation time. From a numerical point of view, the new parameterizations were tested by several computers; about 78% on the average of the computation time was saved when compared with the original algorithm. In other words, on average, about 1.2 s/calculation was gained, which is valuable in climate models simulations when millions of dry deposition calculations are needed.

In Jordan, ~61% of total residential energy consumption is consumed by heating spaces using portable kerosene (K) and liquified petroleum gas (LPG) heaters. Here, we evaluated the indoor air quality (IAQ) versus the use of K and LPG heaters inside a test room reflecting the typical conditions of Jordanian dwellings during the winter season. The experimental setup included particle size distribution (diameter 0.01–25 µm) measurements, and we utilized a simple sectional indoor aerosol model (SIAM) to estimate the emission rate and lifetime of the combustion products in the test room. The particle number (PN) concentration during the LPG operation was 6 × 104–5.9 × 105 cm−3, depending on the setting at minimum, medium, or maximum. The K heater operation increased with the PN concentrations to a range of 4 × 105–8 × 105 cm−3. On average, the particle losses were 0.7–1.6 h−1 for micron particles (1–10 µm) and 0.8–0.9 h−1 for ultrafine particles (10–2.8 × 1010 particles/s (6.6 × 106–8.0 × 106 particles/J), and that for the K heater was about 4.4 × 1010 particles/s (1.9 × 107 particles/J). The results call for the immediate need to apply interventions to improve the IAQ by turning to cleaner heating processes indoors.

Three simple approaches to forecast the COVID-19 epidemic in Jordan were previously proposed by Hussein, et al.: a short-term forecast (STF) based on a linear forecast model with a learning database on the reported cases in the previous 5–40 days, a long-term forecast (LTF) based on a mathematical formula that describes the COVID-19 pandemic situation, and a hybrid forecast (HF), which merges the STF and the LTF models. With the emergence of the OMICRON variant, the LTF failed to forecast the pandemic due to vital reasons related to the infection rate and the speed of the OMICRON variant, which is faster than the previous variants. However, the STF remained suitable for the sudden changes in epi curves because these simple models learn for the previous data of reported cases. In this study, we revisited these models by introducing a simple modification for the LTF and the HF model in order to better forecast the COVID-19 pandemic by considering the OMICRON variant. As another approach, we also tested a time-delay neural network (TDNN) to model the dataset. Interestingly, the new modification was to reuse the same function previously used in the LTF model after changing some parameters related to shift and time-lag. Surprisingly, the mathematical function type was still valid, suggesting this is the best one to be used for such pandemic situations of the same virus family. The TDNN was data-driven, and it was robust and successful in capturing the sudden change in +qPCR cases before and after of emergence of the OMICRON variant.

The Mediterranean region is an important area for air pollution as it is the crossroads between three continents; therefore, the concentrations of atmospheric aerosol particles are influenced by emissions from Africa, Asia, and Europe. Here we concentrate on an eleven-month time series of the ambient concentration of organic carbon (OC) and elemental carbon (EC) between May 2018–March 2019 in Amman, Jordan. Such a dataset is unique in Jordan. The results show that the OC and EC annual mean concentrations in PM2.5 samples were 5.9 ± 2.8 µg m–3 and 1.7 ± 1.1 µg m–3, respectively. It was found that the majority of OC and EC concentrations were within the fine particle fraction (PM2.5). During sand and dust storm (SDS) episodes OC and EC concentrations were higher than the annual means; the mean values during these periods were about 9.6 ± 3.5 µg m–3 and 2.5 ± 1.2 µg m–3 in the PM2.5 samples. Based on this, the SDS episodes were identified to be responsible for an increased carbonaceous aerosol content as well as PM2.5 and PM10 content, which may have direct implications on human health. This study encourages us to perform more extensive measurements during a longer time period and to include an advanced chemical and physical characterization for urban aerosols in the urban atmosphere of Amman, which can be representative of other urban areas in the region.

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Here, we report on a phyto-mediated bimetallic (NiFe2O4) preparation using a Boswellia carterii extract, which was characterized by XRD, FT-IR, TGA, electron microscopy, magnetic spectroscopy, and Mössbauer spectroscopy measurements. The prepared nano-catalysts were tested for oxidation of lignin monomer molecules—vanillyl alcohol and cinnamyl alcohol. In comparison with previously reported methods, the nano NiFe2O4 catalysts showed high photocatalytic activity and selectivity, under visible light irradiation with a nitroxy radical initiator (2,2,6,6-tetramethylpiperidinyloxy or 2,2,6,6-tetramethylpiperidine 1-oxyl; TEMPO) at room temperature and aerobic conditions. The multifold advantages of the catalyst both in terms of reduced catalyst loading and ambient temperature conditions were manifested by higher conversion of the starting material.

The authors would like to correct the published article [...]

Atmospheric new particle formation (NPF) events taking place over large distances between locations, featuring similar characteristics, have been the focus of studies during the last decade. The exact mechanism which triggers NPF still remains indefinable, so are the circumstances under which simultaneous occurrence of such events take place in different environments, let alone in environments which are parted by over 1200 km. In this study, concurrent number size distribution measurements were conducted in the urban environments of Athens (Greece) and Amman (Jordan) as well as the regional background site of Finokalia, Crete, all located within a distance of almost 1300 km for a 6-month period (February–July 2017). During the study period Athens and Finokalia had similar occurrence of NPF (around 20%), while the occurrence in Amman was double. When focusing on the dynamic characteristics at each site, it occurs that formation and growth rates at Amman are similar to those at Finokalia, while lower values in Athens can be ascribed to a higher pre-existing particle number at this urban site. By comparing common NPF events there are 5 concomitant days between all three sites, highly related to air masses origin. Additionally, for another 19 days NPF takes place simultaneously between Finokalia and Amman, which also share common meteorological characteristics, adding to a total of 60% out of 41 NPF events observed at Finokalia, also simultaneously occurring in Amman.

Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions.

Indoor combustion processes associated with cooking, heating, and smoking are a major source of aerosols in Jordanian dwellings. To evaluate human exposure to combustion-generated aerosols in Jordanian indoor environments, regional inhaled deposited dose rates of indoor aerosols (10 nm to 25 µm) were determined for different scenarios for adult occupants. The inhaled deposited dose rate provides an estimate of the number or mass of inhaled aerosol that deposits in each region of the respiratory system per unit time. In general, sub-micron particle number (PN1) dose rates ranged from 109 to 1012 particles/h, fine particle mass (PM2.5) dose rates ranged from 3 to 216 µg/h, and coarse particle mass (PM10) dose rates ranged from 30 to 1600 µg/h. Dose rates were found to be dependent on the type and intensity of indoor combustion processes documented in the home. Dose rates were highest during cooking activities using a natural gas stove, heating via natural gas and kerosene, and smoking (shisha/tobacco). The relative fraction of the total dose rate received in the head airways, tracheobronchial, and alveolar regions varied among the documented indoor combustion (and non-combustion) activities. The significant fraction of sub-100 nm particles produced during the indoor combustion processes resulted in high particle number dose rates for the alveolar region. Suggested approaches for reducing indoor aerosol dose rates in Jordanian dwellings include a reduction in the prevalence of indoor combustion sources, use of extraction hoods to remove combustion products, and improved ventilation/filtration in residential buildings.

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R 2 = 0.77 ) and TDNN for hourly averaged data (with R 2 = 0.66 ) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters.

We characterized new particle formation (NPF) events in the urban background of Amman during August 2016−July 2017. The monthly mean of submicron particle number concentration was 1.2 × 104−3.7 × 104 cm−3 (exhibited seasonal, weekly, and diurnal variation). Nucleation mode (10−15 nm) concentration was 0.7 × 103−1.1 × 103 cm−3 during daytime with a sharp peak (1.1 × 103−1.8 × 103 cm−3) around noon. We identified 110 NPF events (≈34% of all days) of which 55 showed a decreasing mode diameter after growth. The NPF event occurrence was higher in summer than in winter, and events were accompanied with air mass back trajectories crossing over the Eastern Mediterranean. The mean nucleation rate (J10) was 1.9 ± 1.1 cm−3 s−1 (monthly mean 1.6−2.7 cm−3 s−1) and the mean growth rate was 6.8 ± 3.1 nm/h (4.1−8.8 nm/h). The formation rate did not have a seasonal pattern, but the growth rate had a seasonal variation (maximum around August and minimum in winter). The mean condensable vapor source rate was 4.1 ± 2.2 × 105 molecules/cm3 s (2.6−6.9 × 105 molecules/cm3 s) with a seasonal pattern (maximum around August). The mean condensation sink was 8.9 ± 3.3 × 10−3 s−1 (6.4−14.8 × 10−3 s−1) with a seasonal pattern (minimum around June and maximum in winter).

There is limited research on indoor air quality in the Middle East. In this study, concentrations and size distributions of indoor particles were measured in eight Jordanian dwellings during the winter and summer. Supplemental measurements of selected gaseous pollutants were also conducted. Indoor cooking, heating via the combustion of natural gas and kerosene, and tobacco/shisha smoking were associated with significant increases in the concentrations of ultrafine, fine, and coarse particles. Particle number (PN) and particle mass (PM) size distributions varied with the different indoor emission sources and among the eight dwellings. Natural gas cooking and natural gas or kerosene heaters were associated with PN concentrations on the order of 100,000 to 400,000 cm−3 and PM2.5 concentrations often in the range of 10 to 150 µg/m3. Tobacco and shisha (waterpipe or hookah) smoking, the latter of which is common in Jordan, were found to be strong emitters of indoor ultrafine and fine particles in the dwellings. Non-combustion cooking activities emitted comparably less PN and PM2.5. Indoor cooking and combustion processes were also found to increase concentrations of carbon monoxide, nitrogen dioxide, and volatile organic compounds. In general, concentrations of indoor particles were lower during the summer compared to the winter. In the absence of indoor activities, indoor PN and PM2.5 concentrations were generally below 10,000 cm−3 and 30 µg/m3, respectively. Collectively, the results suggest that Jordanian indoor environments can be heavily polluted when compared to the surrounding outdoor atmosphere primarily due to the ubiquity of indoor combustion associated with cooking, heating, and smoking.

Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20−80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86−0.94; urban background: adjR2 = 0.74−0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future.

Black carbon (BC) is an important component of particulate matter (PM) in urban environments. BC is typically emitted from gas and diesel engines, coal-fired power plants, and other sources that burn fossil fuel. In contrast to PM, BC measurements are not always available on a large scale due to the operational cost and complexity of the instrumentation. Therefore, it is advantageous to develop a mathematical model for estimating the quantity of BC in the air, termed a BC proxy, to enable widening of spatial air pollution mapping. This article presents the development of BC proxies based on a Bayesian framework using measurements of PM concentrations and size distributions from 10 to 10,000 nm from a recent mobile air pollution study across several areas of Jordan. Bayesian methods using informative priors can naturally prevent over-fitting in the modelling process and the methods generate a confidence interval around the prediction, thus the estimated BC concentration can be directly quantified and assessed. In particular, two types of models are developed based on their transparency and interpretability, referred to as white-box and black-box models. The proposed methods are tested on extensive data sets obtained from the measurement campaign in Jordan. In this study, black-box models perform slightly better due to their model complexity. Nevertheless, the results demonstrate that the performance of both models does not differ significantly. In practice, white-box models are relatively more convenient to be deployed, the methods are well understood by scientists, and the models can be used to better understand key relationships.

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.

We calculated the regional deposited dose of inhaled particulate matter based on number/mass concentrations in Amman, Jordan. The dose rate was the highest during exercising but was generally lower for females compared to males. The fine particles dose rate was 1010–1011 particles/h (101–102 µg/h). The PM10 dose rate was 49–439 µg/h for males and 36–381 µg/h for females. While resting, the PM10 deposited in the head airways was 67–77% and 8–12% in the tracheobronchial region. When exercising, the head airways received 37–44% of the PM10, whereas the tracheobronchial region received 31–35%. About 8% (exercise) and 14–16% (rest) of the PM2.5 was received in the head airways, whereas the alveolar received 74–76% (exercise) and 54–62% (rest). Extending the results for common exposure scenarios in the city revealed alarming results for service workers and police officers; they might receive PM2.5 and 220 µg/h PM10 while doing their duty on main roads adjacent to traffic. This is especially critical for a pregnant police officer. Outdoor athletic activities (e.g., jogging along main roads) are associated with high PM2.5 and PM10 dose rates (100 µg/h and ~425 µg/h, respectively).

There is a paucity of comprehensive air quality data from urban areas in the Middle East. In this study, portable instrumentation was used to measure size-fractioned aerosol number, mass, and black carbon concentrations in Amman and Zarqa, Jordan. Submicron particle number concentrations at stationary urban background sites in Amman and Zarqa exhibited a characteristic diurnal pattern, with the highest concentrations during traffic rush hours (2−5 × 104 cm−3 in Amman and 2−7 × 104 cm−3 in Zarqa). Super-micron particle number concentrations varied considerably in Amman (1−10 cm−3). Mobile measurements identified spatial variations and local hotspots in aerosol levels within both cities. Walking paths around the University of Jordan campus showed increasing concentrations with proximity to main roads with mean values of 8 × 104 cm−3, 87 µg/m3, 62 µg/m3, and 7.7 µg/m3 for submicron, PM10, PM2.5, and black carbon (BC), respectively. Walking paths in the Amman city center showed moderately high concentrations (mean 105 cm−3, 120 µg/m3, 85 µg/m3, and 8.1 µg/m3 for submicron aerosols, PM10, PM2.5, and black carbon, respectively). Similar levels were found along walking paths in the Zarqa city center. On-road measurements showed high submicron concentrations (>105 cm−3). The lowest submicron concentration (4 cm−3) was observed near a remote site outside of the cities.

Three years of aerosol particle number concentrations (PNCs), size distributions and vertical particle fluxes measured at the semi-urban SMEAR III station in Helsinki, Finland, were studied. The purpose is to study the local emission sources and their effect on particle concentrations and size distributions. By means of cluster analysis, six representative size distributions were identified. Their occurrence together with particle concentrations and fluxes were found to vary significantly with wind direction. Lower particle concentrations and fluxes were measured downwind from vegetated and residential areas compared to directions where the measurement site is downwind from roads passing near the measurement site. For these directions, contributions of the local sources on the measured particle concentrations and size distributions were evident. In particular, size distributions with a mode in the size range 20–40 nm were found to be more affected by local traffic emissions, whereas the mode shifted towards larger sizes when contribution from distant sources was more evident. Using flux footprint functions, mixed vehicle fleet emission factors (EFs) were derived from the particle flux measurements. EFs of 6.03 (±0.19)1014 # Veh−1 km−1 and 3.65 (±0.12)1014 # Veh−1 km−1 were estimated for cold (October–March) and warm (April–September) periods, respectively. Emission factors increased with decreasing air temperature (T) following a linear relationship EF=−0.201014 # Veh−1 km−1°C−1 T+6.981014 # Veh−1 km−1 (RMSE=3.71014 # Veh−1 km−1).