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Heavy air pollution attacked Beijing and its surrounding areas in January of 2013, which attracted large attentions from around the world. In this study, we conducted highly time-resolved measurements of inorganic ions associated with PM2.5 at an urban site in Beijing during this period. We applied the curve fitting method, the quantile regression model and the probability function model to evaluate the relationship between secondary inorganic ions (sulfate, nitrate and ammonium, SNA) and meteorological parameters (relative humidity (RH) and temperature). In our model results, RH was regarded as one of the key factors of high concentrations of SNA, and high level of RH would enhance the concentrations of SNA. In addition, the effect of temperature was also important and noticeable. We further constructed a probability function model to investigate the joint effects of RH and temperature. The model results showed higher RH and a temperature of approximately −5 °C (−4∼-6 °C for sulfate and nitrate; −5∼-7 °C for ammonium) would be the most suitable conditions for the high concentrations of SNA during the air pollution episodes in Beijing.

Modeling fine-scale spatial and temporal patterns of air pollutants can be challenging. Advanced spatio-temporal modeling methods were used to predict both long-term and short-term concentrations of six criteria air pollutants (particulate matter with aerodynamic diameter less than or equal to 10 and 2.5 μm [PM10 and PM2.5], SO2, NO2, ozone and carbon monoxide [CO]) in Beijing, China. Monitoring data for the six criteria pollutants from April 2014 through December 2017 were obtained from 23 administrative monitoring sites in Beijing. The dimensions of a large array of geographic covariates were reduced using partial least squares (PLS) regression. A land use regression (LUR) model in a universal kriging framework was used to estimate pollutant concentrations over space and time. Prediction ability of the models was determined using leave-one-out cross-validation (LOOCV). Prediction accuracy of the spatio-temporal two-week averages was excellent for all of the pollutants, with LOOCV mean squared error-based R2 (R2mse) of 0.86, 0.95, 0.90, 0.82, 0.94 and 0.95 for PM10, PM2.5, SO2, NO2, ozone and CO, respectively. These models find ready application in making fine-scale exposure predictions for members of cohort health studies and may reduce exposure measurement error relative to other modeling approaches.

This study sought to clarify the correlation of individual exposure measurements and PM2.5 measurements collected at regulatory monitoring sites in short-term panel study settings. To achieve this goal, 30 young, healthy adult participants were assigned to three groups with 4 samplers in each group to collect individual exposures during four weekends in March 2016. Participants also completed cardiopulmonary function tests during the same periods. For comparison, ambient air pollution data were obtained from the Air Pollution Surveillance Network in Guangzhou, China. The 8-h ambient pollutant averages and group sampler concentrations were used as separate indicators of air pollution exposure. Results showed that the 8-h mean concentration of personal PM2.5 exposure was 65.09 ± 22.18 μg/m3, which was 24.34 μg/m3 statistically higher than the ambient concentrations over the same period (p

The determination of background concentration of PM2.5 is important to understand the contribution of local emission sources to total PM2.5 concentration. The purpose of this study was to exam the performance of baseline separation techniques to estimate PM2.5 background concentration. Five separation methods, which included recursive digital filters (Lyne-Hollick, one-parameter algorithm, and Boughton two-parameter algorithm), sliding interval and smoothed minima, were applied to one-year PM2.5 time-series data in two heavily polluted cities, Tianjin and Jinan. To obtain the proper filter parameters and recession constants for the separation techniques, we conducted regression analysis at a background site during the emission reduction period enforced by the Government for the 2014 Asia-Pacific Economic Cooperation (APEC) meeting in Beijing. Background concentrations in Tianjin and Jinan were then estimated by applying the determined filter parameters and recession constants. The chemical mass balance (CMB) model was also applied to ascertain the effectiveness of the new approach. Our results showed that the contribution of background PM concentration to ambient pollution was at a comparable level to the contribution obtained from the previous study. The best performance was achieved using the Boughton two-parameter algorithm. The background concentrations were estimated at (27 ± 2) μg/m3 for the whole year, (34 ± 4) μg/m3 for the heating period (winter), (21 ± 2) μg/m3 for the non-heating period (summer), and (25 ± 2) μg/m3 for the sandstorm period in Tianjin. The corresponding values in Jinan were (30 ± 3) μg/m3, (40 ± 4) μg/m3, (24 ± 5) μg/m3, and (26 ± 2) μg/m3, respectively. The study revealed that these baseline separation techniques are valid for estimating levels of PM2.5 air pollution, and that our proposed method has great potential for estimating the background level of other air pollutants.

The role of precursors' concentrations and meteorological conditions on the emerging ozone pollution problem in China has received wide attention, especially after the releasing of the Air Pollution Prevention Control and Action Plan (APPC) since 2013. With the decreasing trend of PM2.5 nationwide, the effect of the strict control measures on increasing ozone variation has less been studied due to the challenge of complexity of nonlinear relationship among a number of factors on ozone formation. This paper evaluated the influence of both ozone precursors and meteorology on maximum daily average 8 h (MDA8) ozone at two urban sites and one rural site in Hebei province, China, from 2013 to 2017, by using a combined application of Kolmogorov-Zurbenko (KZ) filter and artificial neural network (ANN) model. Results showed that R2 was 0.80 between the measured and the simulated MDA8 ozone concentration when using meteorological factors and precursors' concentrations as input variables for ANN model. However, ANN model has limitation in estimating O3 concentration peaks. The values of threat score (TS), probability of detection (POD) and false alarm rate (FAR) for MDA8 ozone concentration were 39%, 44% and 21%, respectively, throughout three studied sites in Hebei province. The annual average percentage change of precursor-related ozone was 0.67% from 2014 to 2017. Temperature, atmospheric pressure and boundary layer height were shown to account for 64% of the variability in long-term ozone levels. Ozone variation in Hebei province was reproduced mainly by meteorological parameters, and the contribution from precursors’ concentration was smaller during the years when the APPC was implemented.

To assess the public health benefits of reducing PM2.5 in Tianjin, we created an annual air quality surface with a land use regression (LUR) model conducted at a high spatial resolution (1 km). The predictors included in the final model were population density, road length within a 1000 m buffer, industrial land area within a 2000m buffer and distance to the coast. The fitting R2 and the leave-one-out-cross-validation (LOOCV) R2 of the PM2.5 LUR models were 0.78 and 0.73, respectively, suggesting that the predicted PM2.5 concentrations fitted well with the measured values for the entire year. Daily air quality surfaces were established based on historic concentration data and interpolation method. We evaluated avoided cases of mortality and morbidity in Tianjin, assuming achievement of China's current air quality daily and annual standards (No. GB3095-2012). Reducing the daily average PM2.5 to the daily Class II standard (75 μg/m3), the avoided emergency department visits, the deaths for cardiovascular disease and the deaths for respiratory disease are 85,000 (95% confidence interval (CI), 17,000–150,000), 2000 (95% CI, 920–3100) and 280 (95% CI, 94–460) per year respectively, and the monetary values are 23–42 million yuan, 180–4800 million yuan and 25–670 million yuan per year in 2015 yuan year respectively. Reducing the annual average PM2.5 to the annual Class II standard (35 μg/m3), the avoided emergency department visits, the deaths for cardiovascular disease and the deaths for respiratory disease are 59,000 (95% CI, 12,000–110,000), 1400 (95% CI, 640–2100) and 200 (95% CI, 66–320) per year respectively, and the monetary values are 16–29 million yuan, 130 to 3400 million yuan and 18 to 480 million yuan per year in 2015 yuan year respectively.

Southern Xinjiang located in the far northwest of China is experiencing serious particulate matter (PM) pollution. The wind erosion has been recognized as a great contributor of PM10 and PM2.5 concentrations in southern Xinjiang. In this study we developed a method that used the Wind Erosion Prediction System (WEPS) to establish the PM10 and PM2.5 emission inventories from the wind erosion source in southern Xinjiang with a 10 km × 10 km spatial resolution and a temporal resolution of one month. The PM10 and PM2.5 emission inventories from wind erosion sources were provided as input to the Models3/SMOKE (Sparse Matrix Operator Kernel Emission). The Community Multi-scale Air Quality (CMAQ) model was employed to simulate PM10 and PM2.5 concentrations from wind erosion source and the results were compared with the monitoring data to verify the emission inventories. The total PM10 and PM2.5 emissions were 12 × 106 t and 4 × 106 t, respectively, and the emission per unit area were 14.6 t/km2 and 4.9 t/km2, respectively, in southern Xinjiang in 2016. The PM10 and PM2.5 emissions per unit area were highest in Kashi being 19.1 t/km2 and 9.1 t/km2, respectively. The total emissions peaked at 5.4 × 106 t/km2 in PM10 and 1.4 × 106 t/km2 in PM2.5 in Bazhou. The PM10 and PM2.5 emissions were highest in spring, followed by summer, winter and autumn. The Normalized Mean Error (NME) between the predicted monthly average PM10 and PM2.5 concentrations from wind erosion source and the results from multiplying the monitoring data by the source apportionment results of PM10 and PM2.5 were 25.3% and 26.1%, respectively, which indicated that the accuracies of the PM10 and PM2.5 emission inventories from wind erosion source in southern Xinjiang were satisfactory.

Epidemiological studies have reported inconsistent and inconclusive associations between long-term exposure to ambient air pollution and lung function in children from Europe and America, where air pollution levels were typically low. The aim of the present study is to examine the relationship between air pollutants and lung function in children selected from heavily industrialized and polluted cities in northeastern China. During 2012, 6740 boys and girls aged 7–14 years were recruited in 24 districts of seven northeastern cities. Portable electronic spirometers were used to measure lung function. Four-year average concentrations of particulate matter with an aerodynamic diameter ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were measured at monitoring stations in the 24 districts. Two-staged regression models were used in the data analysis, controlling for covariates. Overall, for all subjects, the increased odds of lung function impairment associated with exposure to air pollutants, ranged from 5% (adjusted odds ratio [aOR] = 1.05; 95% confidence interval [CI] = 1.01, 1.10) for FVC

A Tethered balloon-based field campaign was launched for the vertical observation of air pollutants within the lower troposphere of 1000 m for the first time over a Chinese megacity, Shanghai in December of 2013. A custom-designed instrumentation platform for tethered balloon observation and ground-based observation synchronously operated for the measurement of same meteorological parameters and typical air pollutants. One episodic event (December 13) was selected with specific focus on particulate black carbon, a short-lived climate forcer with strong warming effect. Diurnal variation of the mixing layer height showed very shallow boundary of less than 300 m in early morning and night due to nocturnal inversion while extended boundary of more than 1000 m from noon to afternoon. Wind profiles showed relatively stagnant synoptic condition in the morning, frequent shifts between upward and downward motion at noon and in the afternoon, and dominant downward motion with sea breeze in the evening. Characteristics of black carbon vertical profiles during four different periods of a day were analyzed and compared. In the morning, surface BC concentration averaged as high as 20 μg/m 3 due to intense traffic emissions from the morning rush hours and unfavorable meteorological conditions. A strong gradient of BC concentrations with altitude was observed from the ground to the top of boundary layer at around 250–370 m. BC gradients turned much smaller above the boundary layer. BC profiles measured during noon and afternoon were the least dependent on heights. The largely extended boundary layer with strong vertical convection was responsible for a well mixing of BC particles in the whole measured column. BC profiles were similar between the early-evening and late-evening phases. The lower troposphere was divided into two stratified air layers with contrasted BC vertical distributions. Profiles at night showed strong gradients from the relatively high surface concentrations to low concentrations near the top of the boundary layer around 200 m. Above the boundary layer, BC increased with altitudes and reached a maximum at the top of 1000 m. Prevailing sea breeze within the boundary layer was mainly responsible for the quick cleanup of BC in the lower altitudes. In contrast, continental outflow via regional transport was the major cause of the enhanced BC aloft. This study provides a first insight of the black carbon vertical profiles over Eastern China, which will have significant implications for narrowing the gaps between the source emissions and observations as well as improving estimations of BC radiative forcing and regional climate.

In this study, filter samples of six Phthalate esters (PAEs) in indoor PM10 and PM2.5 were collected from thirteen homes in Tianjin, China. The results showed that the concentrations of Σ6PAEs in indoor PM10 and PM2.5 were in the range of 13.878–1591.277 ng m−3 and 7.266–1244.178 ng m−3, respectively. Dibutyl phthalate (DBP) was the most abundant compounds followed by di-2-ethylhexyl phthalate (DEHP) in indoor PM10 and PM2.5. Whereas DBP and dimethyl phthalate (DMP) were the predominant compounds in indoor air (gas-phase + particle-phase), the median values were 573.467 and 368.364 ng m−3 respectively. The earlier construction time, the lesser indoor area, the old decoration, the very crowded items coated with plastic and a lower frequency of dusting may lead to a higher level of PAEs in indoor environment. The six PAEs in indoor PM10 and PM2.5 were higher in summer than those in winter. The daily intake (DI) of six PAEs for five age groups through air inhalation in indoor air in Tianjin was estimated. The results indicated that the highest exposure dose was DBP in every age group, and infants experienced the highest total DIs (median: 664.332 ng kg-bw−1 day−1) to ∑6PAEs, whereas adults experienced the lowest total DIs (median: 155.850 ng kg-bw−1 day−1) to ∑6PAEs. So, more attention should be paid on infants in the aspect of indoor inhalation exposure to PAEs.

The mass concentrations and profile characteristic for 18 kinds of polycyclic aromatic hydrocarbons (PAHs) in PM2.5 and PM10 from stack gases for six types of stationary sources in Shandong Province, China were studied by a dilution sampling system and GC–MS analysis method from February to March in 2010. The mass concentrations of PM2.5 and PM10 from the six types of stationary sources varied in 8.2–79.4 mg m−3 and 23.3–156.7 mg m−3, respectively. The total mass concentrations of analyzed PAHs in PM2.5 and PM10 were in the ranges of 0.40–94.35 μg m−3 and 9.16–122.91 μg m−3. The most toxic ashes were from sinter and coke oven for both PM2.5 and PM10 with high carcinogenic PAHs concentrations. BbF, Phe, NaP, BghiP, Pyr, BaP and BeP were abundant which was different from formers and one of the key reasons may be the differences of sampling methods. Diversities in PAHs compositions existed between fly ashes within PM2.5 and PM10 fractions for coke oven according to coefficient of divergence (CD) values. PAHs profiles for PM10 emitted from coke oven were different from those of other stationary sources (with CD values higher than 0.35) and for PM2.5, it was the same for sinter (with most CD values close to 0.30). There existed similar PAHs markers for fine particles emitted from stationary sources excepted for the sinter. For PM10, PAHs markers were primary 3-ring PAHs except for the coke oven with BbF, IND and BghiP as its signatures. Diagnostic ratios of BaA/(BaA + Chr), Flu/(Flu + Pyr), BaP/(BaP + BeP), BeP/BghiP and IND/(IND + BghiP) could be not well distinguished for the six types of stationary sources with the maximum/minimum ratios lower than 2 for both PM2.5 and PM10 of fly ashes which should be not used for source identification studies. The mass concentrations and source profiles of PAHs should be updated timely for size-differentiated fly ashes from various stationary sources by dilution sampling method.

Phthalic acid esters (PAEs) are produced in large amounts throughout the world and are excessively used in various industries, which have posed a serious threat to human health and the environment. An investigation of six major PAEs congeners in atmospheric PM 10 and PM 2.5 was synchronously conducted at seven sites belonging to different functional zones in spring, summer and winter in Tianjin, China in 2010. Results showed that the average concentrations of DMP, DEP, DBP, BBP, DEHP and DOP in PM 10 were 0.88, 0.73, 12.90, 0.15, 98.29 and 0.83 ng m −3 , respectively, and in PM 2.5 , they were 0.54, 0.30, 8.72, 0.08, 75.68 and 0.33 ng m -3 , respectively. DEHP and DBP were the predominant species. The industrial site exhibited highest PAEs values as 135.9 ± 202.8 ng m −3 . In winter, the detected percentages for DOP were low. The other five PAEs concentrations were higher in winter than those in spring and summer, which may be related to the influence of emission sources, meteorological parameters and the chemical–physical characteristic of themselves. Except for DOP, other PAEs were negatively correlated with ambient temperature and the relationships were the best fitted as exponential forms. Significant positive correlations were found for PAEs in PM 2.5 and PM 10 , indicating common sources. The PM 2.5 /PM 10 ratios (0.53–0.70) for the six PAEs concentrations suggested that they were preferentially concentrated in finer particles. Principal component analysis indicated the emission from cosmetics and personal care products, plasticizers and sewage and industrial wastewater may be important sources for PAEs in atmospheric particulate matter in Tianjin.

Thirty re-suspended dust samples were collected from building surfaces in an oilfield city, re-suspended and sampled through PM 2.5 , PM 10 and PM 100 inlets and analyzed for 18 PAHs by GC–MS technique. PAHs concentrations, toxicity and profiles characteristic for different districts and size were studied. PAHs sources were identified by diagnostic ratios and primary component analysis. Results showed that the total amounts of analyzed PAHs in re-suspended dust in Dongying were 45.29, 23.79 and 11.41 μg g −1 for PM 2.5 , PM 10 and PM 100 , respectively. PAHs tended to concentrate in finer particles with mass ratios of PM 2.5 /PM 10 and PM 10 /PM 100 as 1.96 ± 0.86 and 2.53 ± 1.57. The old district with more human activities and long oil exploitation history exhibited higher concentrations of PAHs from both combustion and non-combustion sources. BaP-based toxic equivalent factor and BaP-based equivalent carcinogenic power exhibited decreasing sequence as PM 2.5 > PM 10 > PM 100 suggesting that the finer the particles, the more toxic of the dust. NaP, Phe, Flu, Pyr, BbF and BghiP were the abundant species. Coefficient of divergence analysis implied that PAHs in different districts and size fractions had common sources. Coal combustion, industrial sources, vehicle emission and petroleum were probably the main contributions according to the principal component analysis result.

Particle Exposure Assessment for Community Elderly (PEACE) in Tianjin, China was to characterize personal PM10 exposure, and provide data support for an epidemiological study investigating potential health effects of PM pollution on Chinese elderly population. In this study, a total of 80 elderly participants were recruited for a two-consecutive-day personal exposure measurement, and simultaneously residential indoor, residential outdoor and community PM10 were monitored in the summer and winter of 2009. Personal PM10 concentrations were 192.8 ± 100.6 μg m−3 in summer and 154.6 ± 105.4 μg m−3 in winter. Modeled personal exposures were less than measured personal exposures while a high coefficient of determination (R2) of 0.71 was obtained. Based on measured and modeled exposures, a mean personal cloud of 30.2 μg m−3 was estimated in summer and 16.5 μg m−3 in winter. Moderate correlation emerged between personal and community PM10 concentrations in summer (r = 0.39), and stronger correlation was found in winter (r = 0.82). Analysis of variance (ANOVA) shown that smoking, cooking and cleaning activities did not produce significant effect on personal exposures. Further more, multivariate regression analysis performed in this study revealed that community PM10 level contributed most of personal PM10 exposure, 32% in summer and 64% in winter, respectively. The findings of this study indicated that PM10 personal exposures were considerably influenced by outdoor particulate matter rather than typical indoor sources, and ambient PM10 level measured at community monitoring sites may be used as a surrogate of personal exposure to PM10.

In this study, concentrations of polycyclic aromatic hydrocarbons (PAHs) associated with PM10 were measured to examine the status, characteristics and sources of atmospheric PAH pollution in the industrial Northeast Region of China. Mean concentrations of total PAHs were 65.5, 40.0, 73.0 and 436.7 ng m−3 in the four seasons respectively. The calculated BaPeq concentrations in winter all exceeded the national standard, imposing serious PAH exposure risk. PAH concentrations varied between the cities, but PAH concentrations in different functional areas within a city did not show significant difference. In general, particulate PAH profiles were dominated by 4- and 5-ring compounds. Elevated proportions of 3-ring PAHs and 5-ring PAHs were found in winter and in summer respectively. Diagnostic ratios and principal component analysis (PCA) were used to identify potential sources of PAHs. Coal combustion activities were the main contributors of particle-associated PAHs in this region.

A total of 120 fugitive dust samples were collected to acquire chemical source profiles of PM 10 in Fushun including 27 soil dust samples, 32 road dust samples, 19 construction dust samples, 13 coal storage pile samples, 2 cement production samples, 13 coal-fired power plant fly ash samples, 5 fly ash samples from iron smelt plant and 9 samples from industrial raw material and production piles. The samples were classified as 20 subtypes. The dust samples were dried, sieved, resuspended and sampled through a PM 10 inlet onto filters, and then chemically analyzed. Inductively coupled plasma-atomic emission spectrometry, ion chromatograph and thermal/optical reflectance methods were adopted for analyzing twenty elements including Na, Mg, Al, Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Pb and Hg and nine ions including Na + , NH 4 + , K + , Mg 2+ , Ca 2+ , F − , Cl − , NO 3 − and SO 4 2− as well as OC and EC, respectively. The chemical compositions were compared for 20 subtypes. Si and Ca were the most abundant elements in all the fugitive dust profiles. Enrichment factors of elements in fly ashes compared to raw coal were calculated with Fe as reference element. The highest enriched elements were Ni, Cu, Zn and Pb. Significant difference existed among PM 10 profiles with the coefficient of divergence values ranging from 0.28 to 0.78. Profiles were compared with others. Si exhibited lower content in this study for soil and road dust while EC and Cr showed much higher content compared to others indicating the influence of coal mining and industries activities in Fushun. This was validated by source signatures analysis which indicated almost all the fugitive dust were relative to coal and may also be influenced by metallurgy. The ratios of Mn/V, V/Ni, Zn/Pb and Zn/Cd were calculated for source identification. Elemental ratios may vary widely even for the same source types with different processing courses. Chemical profiles of fugitive dust should be established based on characteristic sources for a specific region and updated timely.

30 re-suspended dust samples were collected from building surfaces of an oilfield city, then re-suspended through PM2.5, PM10 and PM100 inlets and analyzed for 10 metals including V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd and Pb by inductively coupled plasma-mass spectroscopy. Metals concentrations in different fractions and locations were studied. Metals sources were identified by cluster and primary component analysis. The potential risk to human health was assessed by human exposure model. Results showed that Zn, Mn, Pb and Cu were higher in all the three fractions. V, Cr, Mn and Co ranged close to the background values of Chinese soil indicating that they were mainly from crustal materials. Concentrations of Zn, Mn, Pb, V, Cr, Ni, Co and Cd were higher in old district than that in new district for the three fractions. The PM2.5/PM10, PM10/PM100 and PM2.5/PM100 ratios were higher for Zn, Cd, Cu, Pb, Ni, As and Cr (all higher than 1.0), and lower for Co, Mn and V (all less than or close to 1.0) which meant that anthropologic sources associated metals were more easily accumulated in finer particles than metals from crustal materials. Spatial variations indicated that the ten metals peaked at surroundings near railway station, gas stations, industrial boilers and machine manufacturing plant implying the influence of local vehicle emission, fossil fuel combustion and industrial activities as well as crustal materials which was verified by cluster analysis and primary component analysis results. Ingestion of dust particles appeared to be the main route of exposure to re-suspended dust. Hazard Indexes of As were both highest for children and adult which could be a potential threat to human health for non-cancer effect and it also exhibited the highest values for cancer effect as 1.01E-06, 7.04E-07 and 7.21E-07 for PM2.5, PM10 and PM100, respectively.

Polycyclic aromatic hydrocarbons within PM 10 fraction of ashes from two coke production plants, one iron smelt plant, one heating station and one power plant were analyzed with GC–MS technique in 2009. The sum of 17 selected PAHs varied from 290.20 to 7055.72 μg/g and the amounts of carcinogenic PAHs were between 140.33 and 3345.46 μg/g. The most toxic ash was from the coke production plants and then from the iron smelt plant, coal-fired power plant and heating station according to BaP-based toxic equivalent factor (BaPeq) and BaP-based equivalent carcinogenic power (BaPE). PAHs profile of the iron smelt ash was significantly different from others with coefficient of divergence value higher than 0.40. Indicatory PAHs for coke production plants, heating station and coal-fired power plant were mainly 3-ring species such as Acy, Fl and Ace. While for iron smelt plant, they were Chr and BbF. Diagnostic ratios including Ant/(Ant + Phe), Flu/(Flu + Pyr), BaA/Chr, BbF/BkF, Ind/BghiP, IND/(IND + BghiP), BaP/BghiP, BaP/COR, Pyr/BaP, BaA/(BaA + Chr), BaA/BaP and BaP/(BaP + Chr) were calculated which were mostly different from other stacks for the iron smelt plant.

Addition of urea-based antifreeze admixtures during cement mixing in construction of buildings has led to increasing indoor air pollution due to continuous transformation and emission of urea to gaseous ammonia in indoor concrete wall. In order to control ammonia pollution from indoor concrete wall, the aqueous dispersion was firstly prepared with nano-scale TiO 2 photocatalysts and dispersing agent, and then mixed with some textile additives to establish a treating bath or coating paste. Cotton woven fabrics were used as the support materials owing to their large surface area and large number of hydrophilic groups on their cellulose molecules and finished using padding and coating methods, respectively. Two TiO 2 -loaded fabrics were obtained and characterized by X-ray diffractometer (XRD) and scanning electron microscopy (SEM). Moreover, a specifically designed ammonia photocatalytic system consisting of a small environmental chamber and a reactor was used for assessing the performance of these TiO 2 -loaded fabrics as the wall cloth or curtains used in house rooms in the future and some factors affecting ammonia decomposition are discussed. Furthermore, a design equation of surface catalytic kinetics was developed for describing the decomposition of ammonia in air stream. The results indicated that increasing dosage of the TiO 2 aqueous dispersion in treating bath or coating paste improved the ammonia decomposition. And ammonia was effectively removed at low ammonia concentration or gas flow rate. When relative humidity level was 45%, ammonia decomposition was remarkably enhanced. It is the fact that ammonia could be significantly decomposed in the presence of the TiO 2 -padded cotton fabric. Whereas, the TiO 2 -coated cotton fabric had the reduced photocatalytic decomposition of ammonia and high adsorption to ammonia owing to their acrylic binder layer. Finally, the reaction rate constant k and the adsorption equilibrium constant K values were determined through a curve-fitting method and the TiO 2 -padded cotton fabric had the higher k value and lower K value than the TiO 2 -coated cotton fabric.

The authors regret the misspelling of co-author ‘Kahee A. Mohammed’. The authors would like to apologise for any inconvenience caused.