Serotypes and Vaccine Coverage of Streptococcus Pneumoniae Colonization in the Nasopharynx of Thai Children in Congested Areas in Chiang Mai

Streptococcus pneumoniae causes around 10% of all deaths in children younger than five years of age. This study aimed to examine the serogroups/serotypes of S. pneumoniae colonization and vaccine serotype coverage of this organism among Thai children. Nasopharyngeal swabs of children less than or equal to 15 years of age were obtained in congested areas in Chiang Mai from 1 February 2013 to 1 August 2013. The serotyping of S. pneumoniae isolates was performed using the ImmuLex™ kit and the vaccine serotype coverage for this organism was evaluated. A total of 292 children were enrolled. One hundred and thirty children (44.5%) had nasopharyngeal colonization with Streptococcus pneumoniae. Eighty-seven (66.9%) isolates were from children younger than five years of age, seventeen (13.1%) were from children aged 6–10 years, and twenty-six (20%) were from children aged 11–15 years. The five most common serogroups/serotypes isolated were 6 (6A, 6B, 6C) (46.1%), 23 (23F, 23A, 23B) (14.6%), 19 (19F, 19A, 19B, 19C) (8.5%), 15 (15F, 15A, 15B, 15C) (6.9%), and 14 (6.1%). Vaccine serotype coverages in pneumococcal conjugate vaccines (PCV):PCV7, PCV10, and PCV13 were 79.1%, 83.6%, and 85.9%, respectively. There were significant increases in coverage between PCV7 and PCV10 (from 79.1% to 83.6%, p < 0.001), PCV7 and PCV13 (from 79.1% to 85.9%, p < 0.001), and PCV10 and PCV13 (from 83.6% to 85.9%, p < 0.001). The majority of pneumococcal serogroup/serotype colonization in the nasopharynx of Thai children in the studied areas was included in the current licensed pneumococcal conjugated vaccines (PCVs). PCV vaccination should be considered for high-risk children to reduce the incidence of invasive pneumococcal disease among Thai children.

Assessment of Traffic Policemen Exposure To Benzene by Using Trans, Trans-Muconic Acid Biomarker in Ahvaz City, Iran

Benzene is known as an environmental and industrial pollutant. One way to check the amount of exposure to benzene is to measure its metabolites in the urine. Trans, Trans-Muconic Acid which is one of its metabolites is also as a biological indicator of exposure to benzene. In this study, the relationship between the amount of this biomarker and the environmental benzene concentration was investigated in two groups of policemen working outdoors in the traffic-congested areas and indoor in the same area. The results showed that the highest amount of benzene was in Salman Farsi Station at 4.44 ppm. At the same time, the biomarker measured in the urine showed 127.20 μg / L, which was the highest amount. In addition, the amount of benzene in the outdoor was approximately 3 times more than indoor and the amount of biomarker measured in outdoor employees was two times more than indoors ones. In general, wherever there was more benzene, the biomarker would also show a higher amount.

Chemical composition and source characterization of PM10 in urban areas of Lahore, Pakistan

This study aims to assess the physicochemical characteristics of the particulate matter ≤10 µm (PM10) at both congested and non-congested areas of Lahore, the second-largest city of Pakistan. PM10 samples from 10 urban sites in Lahore were analysed for source apportionment. The techniques of scanning electron microscopy/energy dispersive spectrometry (SEM/EDX) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) were used to determine the morphology and the chemical composition of PM10. Thirteen elements including toxic metals were consequently detected and quantified: Ca (48.1%), Zn (17.0%), Fe (13.3%), Al (8.2%), Mg (6.6%), Pb (5.5%), Mn (0.4%), Cu (0.3%), Ba (0.17%), Cd (0.15%), Ni (0.04%), Cr (0.01%) and Co (0.008%). The results showed that the daily PM10 concentration was 6%–9% higher than the World Health Organization’s guideline values at all urban sites of Lahore. The congested sites showed higher contents than the non-congested areas for most of the elements, including Cd (41.8%), Cr (35.0%), Zn (19.7%), Cu (12.7%), Ni (6.2%), Ca (3.4%), Ba (1.2%), Mg (1.2%) and Al (0.07%). The non-congested areas showed higher contents only for Pb (0.07%) and Co (4.3%). The principal component analysis indicated that 72% of PM10 originates from road dust and vehicular sources, and 38% from industrial sources.

Travel management optimization based on air pollution condition using Markov decision process and genetic algorithm (case study: Shiraz city)

Currently, air pollution and energy consumption are the main issues in the transportation area in large urban cities. In these cities, most people choose their transportation mode according to corresponding utility including traveller's and trip’s characteristics. Also, there is no effective solution in terms of population growth, urban space, and transportation demands, so it is essential to optimize systematically travel demands in the real network of roads in urban areas, especially in congested areas. Travel Demand Management (TDM) is one of the well-known ways to solve these problems. TDM defined as a strategy that aims to maximize the efficiency of the urban transport system by granting certain privileges for public transportation modes, Enforcement on the private car traffic prohibition in specific places or times, increase in the cost of using certain facilities like parking in congested areas. Network pricing is one of the most effective methods of managing transportation demands for reducing traffic and controlling air pollution especially in the crowded parts of downtown. A little paper may exist that optimize urban transportations in busy parts of cities with combined Markov decision making processes with reward and evolutionary-based algorithms and simultaneously considering customers’ and trip’s characteristics. Therefore, we present a new network traffic management for urban cities that optimizes a multi-objective function that related to the expected value of the Markov decision system’s reward using the Genetic Algorithm. The planned Shiraz city is taken as a benchmark for evaluating the performance of the proposed approach. At first, an analysis is also performed on the impact of the toll levels on the variation of the user and operator cost components, respectively. After choosing suitable values for the network parameters, simulation of the Markov decision process and GA is dynamically performed, then the optimal decision for the Markov decision process in terms of total reward is obtained. The results illustrate that the proposed cordon pricing has significant improvement in performance for all seasons including spring, autumn, and winter.