ORCID Profile
0000-0002-0241-6435
Current Organisations
University of Jordan
,
University of Helsinki
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Publisher: MDPI AG
Date: 07-04-2022
Abstract: 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.
Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.SCITOTENV.2019.02.398
Abstract: Poor air quality is a leading contributor to the global disease burden and total number of deaths worldwide. Humans spend most of their time in built environments where the majority of the inhalation exposure occurs. Indoor Air Quality (IAQ) is challenged by outdoor air pollution entering indoors through ventilation and infiltration and by indoor emission sources. The aim of this study was to understand the current knowledge level and gaps regarding effective approaches to improve IAQ. Emission regulations currently focus on outdoor emissions, whereas quantitative understanding of emissions from indoor sources is generally lacking. Therefore, specific indoor sources need to be identified, characterized, and quantified according to their environmental and human health impact. The emission sources should be stored in terms of relevant metrics and statistics in an easily accessible format that is applicable for source specific exposure assessment by using mathematical mass balance modelings. This forms a foundation for comprehensive risk assessment and efficient interventions. For such a general exposure assessment model we need 1) systematic methods for indoor aerosol emission source assessment, 2) source emission documentation in terms of relevant a) aerosol metrics and b) biological metrics, 3) default model parameterization for predictive exposure modeling, 4) other needs related to aerosol characterization techniques and modeling methods. Such a general exposure assessment model can be applicable for private, public, and occupational indoor exposure assessment, making it a valuable tool for public health professionals, product safety designers, industrial hygienists, building scientists, and environmental consultants working in the field of IAQ and health.
Publisher: Elsevier BV
Date: 12-2017
DOI: 10.1016/J.SCITOTENV.2017.05.211
Abstract: Floor dust s les were collected from Jordanian indoor environments (eight dwellings and an educational building) in Amman. Quantitative PCR (qPCR) analyses of selected fungal and bacterial groups were performed. The bacterial and fungal concentrations were also correlated with PAHs concentrations, which were previously measured in the same s les by using GC-MS. The bacterial and fungal concentrations varied significantly among and within the tested indoor environments. Based on the collected s les in the entrance area of the dwellings, the largest variation was found in Gram-negative bacteria and total fungi concentration. The lowest bacterial and fungal concentrations were found in the dwelling that was least occupied and the most recently built. At the educational building, the Gram-positive bacteria concentrations were lower than those observed in the dwellings. Unlike for bacteria, we observed significant negative correlation with some polycyclic aromatic hydrocarbons (PAHs). This calls for further studies investigating biodegradation of PAHs in house dust and presence of potentially health hazardous PAH metabolites. Since biocontamination in floor dust has been given relatively little to no attention in the MENA region we recommend that more extensive measurements be conducted in the future with chemical and biological analysis of floor dust contaminants and their exposure indoors.
Publisher: Elsevier BV
Date: 10-2011
Publisher: MDPI AG
Date: 19-11-2019
DOI: 10.3390/APP9224976
Abstract: 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 c aign 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.
Publisher: Springer Science and Business Media LLC
Date: 29-03-2017
DOI: 10.1007/S11356-017-8870-3
Abstract: The quality and chemical composition of urban dew collections with dust precipitates without pre-cleaning of the collecting surface WSF (white standard foil) were investigated for 16 out of 20 collected s les with collected volumes ranging from 22 to 230 ml. The collection period was from March to July 2015 at an urban area, Jubaiha, which is located in the northern part of the capital city Amman, Jordan. The obtained results indicated the predominance of Ca
Publisher: Copernicus GmbH
Date: 03-08-2007
Abstract: Abstract. This study presents an evaluation and modeling exercise of the size fractionated aerosol particle number concentrations measured nearby a major road in Helsinki during 23 August–19 September 2003 and 14 January–11 February 2004. The available information also included electronic traffic counts, on-site meteorological measurements, and urban background particle number size distribution measurement. The ultrafine particle (UFP, diameter nm) number concentrations at the roadside site were approximately an order of magnitude higher than those at the urban background site during daytime and downwind conditions. Both the modal structure analysis of the particle number size distributions and the statistical correlation between the traffic density and the UFP number concentrations indicate that the UFP were evidently from traffic related emissions. The modeling exercise included the evolution of the particle number size distribution nearby the road during downwind conditions. The model simulation results revealed that the evaluation of the emission factors of aerosol particles might not be valid for the same site during different time.
Publisher: Oxford University Press (OUP)
Date: 08-08-2021
Abstract: STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For ex le, for dusts the calibration data were pooled from exposure measurements s led from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is & .6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Hindawi Limited
Date: 06-2022
DOI: 10.1111/INA.13039
Abstract: The IPCC 2021 report predicts rising global temperatures and more frequent extreme weather events in the future, which will have different effects on the regional climate and concentrations of ambient air pollutants. Consequently, changes in heat and mass transfer between the inside and outside of buildings will also have an increasing impact on indoor air quality. It is therefore surprising that indoor spaces and occupant well-being still play a subordinate role in the studies of climate change. To increase awareness for this topic, the Indoor Air Quality Climate Change (IAQCC) model system was developed, which allows short and long-term predictions of the indoor climate with respect to outdoor conditions. The IAQCC is a holistic model that combines different scenarios in the form of submodels: building physics, indoor emissions, chemical-physical reaction and transformation, mold growth, and indoor exposure. IAQCC allows simulation of indoor gas and particle concentrations with outdoor influences, indoor materials and activity emissions, particle deposition and coagulation, gas reactions, and SVOC partitioning. These key processes are fundamentally linked to temperature and relative humidity. With the aid of the building physics model, the indoor temperature and humidity, and pollutant transport in building zones can be simulated. The exposure model refers to the calculated concentrations and provides evaluations of indoor thermal comfort and exposure to gaseous, particulate, and microbial pollutants.
Publisher: Wiley
Date: 24-01-2011
DOI: 10.1002/ENV.1020
Publisher: Hindawi Limited
Date: 2012
DOI: 10.1155/2012/243603
Publisher: MDPI AG
Date: 02-07-2021
Abstract: 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.
Publisher: Elsevier BV
Date: 12-2013
Publisher: MDPI AG
Date: 23-09-2019
Abstract: In this study, we performed elemental analysis for floor dust s les collected in Jordanian microenvironments (dwellings and educational building). We performed intercorrelation and cluster analysis between the elemental, polyaromatic hydrocarbon (PAH), and microorganism concentrations. In general, the educational building workshops had the highest elemental contamination. The age of the dwelling and its occupancy played a role on the elemental contamination level: older and more occupied dwellingshad greater contamination. The elemental contamination at a dwelling entrance was observed to be higher than in the living room. We found exceptionally high concentrations for Fe and Mn in the educational workshop and additionally, Hg, Cr, and Pb concentrations exceeded the limits set by the Canadian Council of Ministers of the Environment. According to the cluster analysis, we found three major groups based on location and contamination. According to the enrichment factor (EF) assessment, Al, Co, Mn, Ti, and Ba had EF 2 (i.e., minimal enrichment) whereas P, S, Pb, Sb, Mo, Zn, Hg, and Cu had EF 40 (i.e., extremely enriched). In contrast, Ca and P were geogenically enriched. Furthermore, significant Spearman correlations indicated nine subgroups of elemental contamination combined with PAHs and microbes.
Publisher: Institute of Mathematical Statistics
Date: 03-2014
DOI: 10.1214/13-AOAS678
No related grants have been discovered for Tareq Hussein.