Estimation of the PM2.5 effective hygroscopic parameter and water content based on particle chemical composition: Methodology and case study

Particle hygroscopicity plays a key role in understanding the mechanisms of haze formation and particle optical properties. The present study developed a method for predicting the effective hygroscopic parameter k and the water content of PM_(2.5) on the basis of the k-K?hler theory and bulk chemical components of PM_(2.5). Our study demonstrated that the effective hygroscopic parameter can be estimated using the PM_(2.5) mass concentration, water-soluble ions, and total water-soluble carbon. By combining the estimated k and ambient relative humidity, the water content of PM_(2.5) can be further estimated. As an example, the k and water content of PM_(2.5) in Beijing were estimated utilizing the method proposed in this study. The annual average value of k of PM_(2.5) in Beijing was 0.25±0.09, the maximum k value 0.26±0.08 appeared in summer, and the seasonal variation is insignificant. The PM_(2.5) water content was determined by both the PM_(2.5) hygroscopicity and the ambient relative humidity(RH). The annual average mass ratio of water content and PM_(2.5) was 0.18±0.20, and the maximum value 0.31±0.25 appeared in summer. Based on the estimated water content of PM_(2.5) in Beijing, the relationship between the PM_(2.5) water content and RH was parameterized as: m(%)=0.03+(5.73×10~(-8)) ×RH~(3.72).This parametric formula helps to characterize the relationship between the PM_(2.5) mass concentration and atmospheric visibility.     

Visibility and Incidence of Respiratory Diseases During the 1998 Haze Episode in Brunei Darussalam

— Air pollution episodes as a result of forest fires in Brunei Darussalam and neighbouring regions have reached hazardous levels in recent years. Such episodes are generally associated with poor visibility and air quality conditions. In the present study, data on PM₁₀ (particulate matter of size less than 10 microns) and CO in Brunei Darussalam have been considered to study the incidence of respiratory diseases whereas data on relative humidity (RH) in addition to PM₁₀ have been used to explain the visibility with a particular emphasis on haze episode during 1998.¶Initial exploratory analysis indicates significant correlation of visibility with PM₁₀ and RH. An attempt has been made to explain visibility on the basis of PM₁₀ and RH using multiple linear regression analysis. The regression model shows that PM₁₀ and RH are two significant factors affecting the visibility at a given site. Further, canonical correlation, a multivariate method of analysis, has been used to explain the incidence of respiratory diseases as a function of air quality during the haze period. The results indicate that PM₁₀ and CO levels during the haze period have a significant bearing on the incidence of respiratory diseases (Asthma, Acute Respiratory Infections and Influenza (ARII)).     

On the Causes of Frequency-Dependent Apparent Seismological Q

Variability of the Earth’s structure makes a first-order impact on attenuation measurements which often does not receive adequate attention. Geometrical spreading (GS) can be used as a simple measure of the effects of such structure. The traditional simplified GS compensation is insufficiently accurate for attenuation measurements, and the residual GS appears as biases in both Q ₀ and η parameters in the frequency-dependent attenuation law Q(f) = Q ₀ f ^η . A new interpretation approach bypassing Q(f) and using the attenuation coefficient χ(f) = γ + πf/Q _e(f) resolves this problem by directly measuring the residual GS, denoted γ, and effective attenuation, Q _e. The approach is illustrated by re-interpreting several published datasets, including nuclear-explosion and local-earthquake codas, Pn, and synthetic 50–300-s surface waves. Some of these examples were key to establishing the Q(f) concept. In all examples considered, χ(f) shows a linear dependence on the frequency, γ ≠ 0, and Q _e can be considered frequency-independent. Short-period crustal body waves are characterized by positive γ _SP values of (0.6–2.0) × 10^?2 s^?1 interpreted as related to the downward upper-crustal reflectivity. Long-period surface waves show negative γ _LP ≈ ?1.9 × 10^?5 s^?1, which could be caused by insufficient modeling accuracy at long periods. The above γ values also provide a simple explanation for the absorption band observed within the Earth. The band is interpreted as apparent and formed by levels of Q _e ≈ 1,100 within the crust decreasing to Q _e ≈ 120 within the uppermost mantle, with frequencies of its flanks corresponding to γ _LP and γ _SP. Therefore, the observed absorption band could be purely geometrical in nature, and relaxation or scattering models may not be necessary for explaining the observed apparent Q(f). Linearity of the attenuation coefficient suggests that at all periods, the attenuation of both Rayleigh and Love waves should be principally accumulated at the sub-crustal depths (~38–100 km).     

The238U spontaneous fission decay constant re-determined by fission tracks

The decay constant(λ_f²³⁸) for the spontaneous fission of²³⁸U was re-determined by means of a man-made uranium glass of known age (126 yr). The spontaneous U fission tracks that had accumulated since the date of manufacture were counted on internal faces of the glass with an error of less than 1.7%. No thermal annealing of the spontaneous tracks was observed. The U content was determined by induced fission tracks. The value obtained forλ_f²³⁸ is(8.57 ± 0.42) × 10^?17yr^?1. Main sources of error are the date of glass melting and the determination of the thermal neutron dose.     

Trends and variability in African long-term precipitation

African precipitation trends are commonly analyzed using short-term data observed over small areas. This study analyzed changes in long-term (1901–2015) annual and seasonal precipitation of high spatial (0.5°?×?0.5° grid) resolution covering the entire African continent. To assess an acceleration/deceleration of the precipitation increase/decrease, trend magnitude (mm/year) over the period 1991–2015 was subtracted from that of 1965–1990 to obtain Slope Difference (SD, mm/year). Co-variation of precipitation sub-trends with changes in large-scale ocean–atmosphere conditions was investigated. Regardless of the trend significance, in most parts of Africa, annual precipitation exhibited negative (positive) trends over the period 1965–1990 (1991–2015). Thus, the continent was, on average, recently (from 1991 to 2015) wetter than it was over the period 1965–1990. From 1901 to 2015, the null hypothesis H₀ (no trend) was rejected (p < 0.05) for annual precipitation decrease over West Africa especially along the coastal areas near the Gulf of Guinea. The H₀ was also rejected (p < 0.05) for the increase in annual and September–November precipitation of some areas along the Equatorial region (such as in Gabon and around Lake Victoria). For both annual and seasonal precipitation, the least SD values in the range ??1 to 1 mm/year were obtained in areas north of 10° N. The SD value went up to about 20 mm/year over the Sahel belt especially for the peak monsoon (June–August season). For the March–May precipitation, positive SD values were obtained in the Western part of Southern Africa. However, negative SD values (around ??5 mm/year) were obtained in the Horn of Africa. Variation in sub-trends of the East African precipitation was found to be driven by changes in Sea Surface Temperature (SST) of the Indian and Atlantic Oceans. Variability in sub-trends of the West African precipitation is linked to changes in SST of the Atlantic Ocean. Changes in sub-trends of the South African precipitation correspond to anomalies in SST from the Pacific and Indian Oceans. Knowledge of precipitation changes and possible drivers is vital for predictive adaptation regarding the impacts of climate variability on hydro- or agro-meteorology.     

Determination of Q β(f) in Different Parts of Kumaon Himalaya from the Inversion of Spectral Acceleration Data

This paper presents the results of a modified two-step inversion algorithm approach to find S wave quality factor Q _β(f) given by Joshi (Bull Seis Soc Am 96:2165–2180, 2006). Seismic moment is calculated from the source displacement spectra of the S wave using both horizontal components. Average value of seismic moment computed from two horizontal components recorded at several stations is used as an input to the first part of inversion together with the spectra of S phase in the acceleration record. Several values of the corner frequency have been selected iteratively and are used as inputs to the inversion algorithm. Solution corresponding to minimum root mean square error (RMSE) is used for obtaining the final estimate of Q _β(f) relation. The estimates of seismic moment, corner frequency and Q _β(f) from the first part of inversion are further used for obtaining the residual of theoretical and observed source spectra which are treated as site amplification terms. The acceleration record corrected for the site amplification term is used for determination of seismic moment from source spectra by using Q _β(f) obtained from first part of inversion. Corrected acceleration record and new estimate of seismic moment are used as inputs to the second part of the inversion scheme which is similar to the first part except for use of input data. The final outcome from this part of inversion is a new Q _β(f) relation together with known values of seismic moment and corner frequency of each input. The process of two-step inversion is repeated for this new estimate of seismic moment and goes on until minimum RMSE is obtained which gives final estimate of Q _β(f) at each station and corner frequency of input events. The Pithoragarh district in the state of Uttarakhand in India lies in the border region of India and Nepal and is part of the seismically active Kumaon Himalaya zone. A network of eight strong motion recorders has been installed in this region since March, 2006. In this study we have analyzed data from 18 local events recorded between March, 2006 and October, 2010 at various stations. These events have been located using HYPO71 and data has been used to obtain frequency-dependent shear-wave attenuation. The Q _β(f) at each station is calculated by using both the north-south (NS) and east-west (EW) components of acceleration records as inputs to the developed inversion algorithm. The average Q _β(f) values obtained from Q _β(f) values at different stations from both NS and EW components have been used to compute a regional average relationship for the Pithoragarh region of Kumaon Himalaya of form Q _β(f)?=?(29?±?1.2)f ^{(1.1 ± 0.06)}.     

Long-term variations in the second sectorial Stokes harmonics on the basis of TOPEX/POSEIDON altimetry between 1993 and 2000

The long-term variations in the second degree sectorial Stokes parameters of the geopotential have been determined from TOPEX-POSEIDON (T/P) satellite altimeter data, covering the period of January 1, 1993 to January 3, 2001 (T/P cycles 11-305). It is the first attempt to determine the variations in the second sectorial harmonics in the Earth’s inertia tensor due to the ocean dynamics. The variations amount to about 1 × 10⁻¹⁰ (J ₂ ⁽²⁾ ≈ 1.6 × 10⁻⁶ and S ₂ ⁽²⁾ ≈ −0.9 × 10⁻⁶). The variations are about 5% of the tidal effect. This corresponds to variations in the directions of the equatorial axes of the Earth’s inertia ellipsoid of up to 10 arc-seconds. Consequently, the annual and semi-annual variations of the Earth’s equatorial flattening is about 10⁻⁹; i.e. it corresponds to a change of 8 units of its denominator of 91 030. (The equatorial flattening ≈ 1/91 030). Since the coverage of the Earth’s ocean surface is not worldwide, and the inclination of T/P is i = 66°, it is only 58.2% (min. depth of the ocean 2 000 m) of the Earth’s surface which is processed, the torque, resulting from the seasonal transfer of masses within a sea surface layer, is not zero. It amounts up to 10¹⁶ kg m²s⁻², which is comparable to the total indirect tidal torque due to the Moon and the Sun, ∼ 3.9 × 10¹⁶ kg m²s⁻². However, the above estimate strongly depends on the adopted thickness of the sea surface layer, ΔR = 50 m. For a larger thickness of ΔR = 100 m, the seasonal torque amounts to about ∼ 2.3 × 10¹⁶ kg m²s⁻².     

Attenuation of High-Frequency Seismic Waves in Eastern Iran

We investigated the frequency-dependent attenuation of the crust in Eastern Iran by analysis data from 132 local earthquakes having focal depths in the range of 5–25 km. We estimated the quality factor of coda waves (Q _c) and body waves (Q _p and Q _s) in the frequency band of 1.5–24 Hz by applying the single backscattering theory of S-coda envelopes and the extended coda-normalization method, respectively. Considering records from recent earthquakes (Rigan M _w 6.5, 2010/12/20, Goharan M _w 6.2, 2013/5/11 and Sirch M _w 5.5, 2013/1/21), the estimated values of Q _c, Q _p and Q _s vary from 151 ± 49, 63 ± 6, and 93 ± 14 at 1.5 Hz to 1,994 ± 124, 945 ± 84 and 1,520 ± 123 at 24 Hz, respectively. The average frequency-dependent relationships (Q = Q _o f ⁿ ) estimated for the region are Q _c = (108 ± 10)f ^(0.96±0.01), Q _p = (50 ± 5)f ^(1.01±0.04), and Q _s = (75 ± 6)f ^(1.03±0.06). These results evidenced a frequency dependence of the quality factors Q _c, Q _p, and Q _s, as commonly observed in tectonically active zones characterized by a high degree of heterogeneity, and the low value of Q indicated an attenuative crust beneath the entire region.     

Monitoring temporal–spatial variations of AOD over the Yangtze River Delta,China

Aerosol can induce visibility reduction, affect radiation balance and modify cloud property on the environmental effect, and show the harmful effects on human health. Insight of aerosol becomes an integral task in the process of control measures for environmental pollution. The present study provided an analysis of temporal–spatial variations of aerosol optical depth (AOD) using the MOD04 level-2 in collection 6 (C6) with the deep blue retrieval algorithm from January 2005 to December 2015 over Yangtze River Delta (YRD) in China. The AOD validations between MODIS and Aerosol Robotic Network (AERONET) were estimated by the methods of regression, correlation. Then, the periodic features and trends of AOD and angstrom exponent (AE) were explored with the wavelet transformation (WT) procedure. Further, the variations of AOD and AE spatial distribution on multi-time scales (annual, monthly and season) were demonstrated. Meantime, the sources of AOD are discussed. It was found that the daily AOD from MODIS has a strong correlation relationship (slope?=?0.9838, r?=?0.84) with AERONET over YRD. The variations of both AOD and AE on time series have been distinct temporal periodic (12, 6 and 4 months) characteristics, and show the decreasing trends on annual and semi-annual periods. On annual, the AOD on spatial distribution is slowly declining from the northwest towards the southeast, and the AE on spatial distribution is gradually decreasing from the northwest to the southeast and from the land to the coast. The variations both inter-annual AOD and AE on spatial distribution show the inverse trends, respectively. On monthly, the means of AOD range from minimum 0.46 in January to maximum 0.90 in July, and the variations of spatial distribution mainly occur in the north parts of Yangtze River and some scattered areas with high terrain and south coast. The means of AE range from minimum 1.13 in October to maximum 1.58 in April, and the variations of spatial distribution are mainly found in the south of Henan, the north of Jiangsu, the coast belt and the riverside of Yangtze River and the high terrain regions. On seasonality, the means of AOD reaches its maximum 0.68 in summer and minimum 0.50 in winter, and the variations of spatial distribution mainly occur in the coast belt, the north parts of Hongze Lake and the south parts with high terrain. The means of AE reaches its maximum 1.48 in spring and minimum 1.25 in autumn, and the variations of spatial distribution were shown the similarity with that of monthly.     

Fine Particles (PM2.5) in Residential Areas of Lucknow City and Factors Influencing the Concentration

Urban populations are exposed to a high level of fine and ultrafine particles from motor vehicle emissions which affect human health. To assess the hourly variation of fine particle (PM_2.5) concentration and the influence of temperature and relative humidity (RH) on the ambient air of Lucknow city, monitoring of PM_2.5 along with temperature and RH was carried out at two residential locations, namely Vikas Nagar and Alambagh, during November 2005. The 24 h mean PM_2.5 concentration at Alambagh was 131.74 μg/m³ and showed an increase of 13.74%, which was significantly higher (p < 0.05) than the Vikas Nagar level. The 24 h mean PM_2.5 on weekdays for both locations was found to be 142.74 μg/m³ (an increase of 66.23%) which was significantly higher (p < 0.01) than the weekend value, indicating that vehicular pollution is one of the important sources of PM_2.5. The mean PM_2.5 at night for all the monitoring days was 157.69 μg/m³ and was significantly higher (p < 0.01) than the daytime concentration (89.87 μg/m³). Correlation and multiple regressions showed that the independent variables, i. e., time, temperature, and RH together accounted for 54%, whereas RH alone accounted for 53% of total variations of PM_2.5, suggesting that RH is the best influencing variable to predict the PM_2.5 concentration in the urban area of Lucknow city. The 24 h mean PM_2.5 for all the monitoring days was found to be higher than the NAAQS recommended by the US‐EPA (65 μg/m³) and can be considered to be an alarming indicator of adverse health effects for city dwellers.     

Predicting spatio-temporal concentrations of PM<Subscript>2.5</Subscript> using land use and meteorological data in Yangtze River Delta,China

The prediction of PM_2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM_2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM_2.5 concentrations. We validated the estimated effects by using the cross-validated R² and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM_2.5 concentration, with a reasonably large R² of 0.86 and a small RMSE of 8.15 (μg/m³). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM_2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.     

Features of Radiation and Propagation of Seismic Waves in the Northern Caucasus: Manifestations in Regional Coda

Based on the Anapa (ANN) seismic station records of ~40 earthquakes (M_W > 3.9) that occurred within ~300 km of the station since 2002 up to the present time, the source parameters and quality factor of the Earth’s crust (Q(f)) and upper mantle are estimated for the S-waves in the 1–8 Hz frequency band. The regional coda analysis techniques which allow separating the effects associated with seismic source (source effects) and with the propagation path of seismic waves (path effects) are employed. The Q-factor estimates are obtained in the form Q(f) = 90 × f 0.7 for the epicentral distances r < 120 km and in the form Q(f) = 90 × f1.0 for r > 120 km. The established Q(f) and source parameters are close to the estimates for Central Japan, which is probably due to the similar tectonic structure of the regions. The shapes of the source parameters are found to be independent of the magnitude of the earthquakes in the magnitude range 3.9–5.6; however, the radiation of the high-frequency components (f > 4–5 Hz) is enhanced with the depth of the source (down to h ~ 60 km). The estimates Q(f) of the quality factor determined from the records by the Sochi, Anapa, and Kislovodsk seismic stations allowed a more accurate determination of the seismic moments and magnitudes of the Caucasian earthquakes. The studies will be continued for obtaining the Q(f) estimates, geometrical spreading functions, and frequency-dependent amplification of seismic waves in the Earth’s crust in the other regions of the Northern Caucasus.     

Attenuation coefficients of Rayleigh and <Emphasis Type="Italic">Lg</Emphasis> waves

Analysis of the frequency dependence of the attenuation coefficient leads to significant changes in interpretation of seismic attenuation data. Here, several published surface-wave attenuation studies are revisited from a uniform viewpoint of the temporal attenuation coefficient, denoted by χ. Theoretically, χ( f) is expected to be linear in frequency, with a generally non-zero intercept γ?=?χ(0) related to the variations of geometrical spreading, and slope dχ/df = π/Q _e caused by the effective attenuation of the medium. This phenomenological model allows a simple classification of χ( f) dependences as combinations of linear segments within several frequency bands. Such linear patterns are indeed observed for Rayleigh waves at 500–100-s and 100–10-s periods, and also for Lg from ~2 s to ~1.5 Hz. The Lg χ( f) branch overlaps with similar linear branches of body, Pn, and coda waves, which were described earlier and extend to ~100 Hz. For surface waves shorter than ~100 s, γ values recorded in areas of stable and active tectonics are separated by the levels of \(\gamma _{D} \approx 0.2 \times 10^{-3}\) s^???1 (for Rayleigh waves) and 8 ×10^???3 s^???1 (for Lg). The recently recognized discrepancy between the values of Q measured from long-period surface waves and normal-mode oscillations could also be explained by a slight positive bias in the geometrical spreading of surface waves. Similarly to the apparent χ, the corresponding linear variation with frequency is inferred for the intrinsic attenuation coefficient, χ _i , which combines the effects of geometrical spreading and dissipation within the medium. Frequency-dependent rheological or scattering Q is not required for explaining any of the attenuation observations considered in this study. The often-interpreted increase of Q with frequency may be apparent and caused by using the Q-based model of attenuation and following preferred Q( f) dependences while ignoring the true χ( f) trends within the individual frequency bands.     

Attenuation of Coda Waves in the Saurashtra Region, Gujarat (India)

The attenuation characteristics based on coda waves of two areas—Jamnagar and Junagarh of Saurashtra, Gujarat (India)—have been investigated in the present study. The frequency dependent relationships have been developed for both the areas using single back scattering model. The broadband waveforms of the vertical components of 33 earthquakes (Mw 1.5–3.5) recorded at six stations of the Jamnagar area, and broadband waveforms of 68 earthquakes (Mw 1.6–5) recorded at five stations of the Junagarh area have been used for the analysis. The estimated relations for the Junagarh area are: Q _c?=?(158?±?5)f^(0.99±0.04) (lapse time : 20?s), Q _c?=?(170?±?4.4)f^(0.97±0.02) (lapse time : 30?s) and Q _c?=?(229?±?6.6)f^(0.94±0.03) (lapse time : 40?s) and for the Jamnagar area are: Q _c?=?(178?±?3)f^(0.95±0.05) (lapse time : 20?s), Q _c?=?(224?±?6)f^(0.98±0.06) (lapse time : 30?s) and Q _c?=?(282?±?7)f^(0.91±0.03) (lapse time : 40?s). These are the first estimates for the areas under consideration. The Junagarh area appears to be more attenuative as compared to the Jamnagar area. The increase in Q _c values with lapse time found here for both the areas show the depth dependence of Q _c as longer lapse time windows will sample larger area. The rate of decay of attenuation (Q ^?1) with frequency for the relations obtained here is found to be comparable with those of other regions of the world though the absolute values differ. A comparison of the coda-Q estimated for the Saurashtra region with those of the nearby Kachchh region shows that the Saurashtra region is less heterogeneous. The obtained relations are expected to be useful for the estimation of source parameters of the earthquakes in the Saurashtra region of Gujarat where no such relations were available earlier. These relations are also important for the simulation of earthquake strong ground motions in the region.     

Variation of intrinsic and scattering attenuation of seismic waves with depth in the Bam region,East-Central Iran

The attenuation properties of the lithosphere in the Bam region, East-Central Iran, have been investigated. For this purpose, 42 local earthquakes having focal depths less than 25 km have been used. The quality factor of coda waves (Q_c) has been estimated using the single back-scattering model. The quality factors Q_p, Q_d (P and direct S-waves) have been estimated using the extended-coda normalization method. Q_i and Q_s (the intrinsic and scattering attenuation parameters) have been estimated for the region. The values of Q_p, Q_d, Q_c, Q_i and Q_s show a dependence on frequency in the range of 1.5–24 Hz for the Bam region. The average frequency-dependent relationships estimated for the region are Q_p=(36±6)f^(1.03±0.06), Q_d=(59±8)f^(1.00±0.03), Q_c=(79±5)f^(1.01±0.04), Q_s=(131±4)f^(1.01±0.04) and Q_i=(104±6)f^(1.01±0.05). A comparison between Q_i and Q_s shows that intrinsic absorption is predominant over scattering.The variation of Q has also been estimated at different lapse times to observe heterogeneities variation with depth. The variation of Q with frequency and lapse time shows that the lithosphere becomes more homogeneous with depth.The estimated Q_o values at different stations suggest a low value of Q indicating a heterogeneous and attenuative crust beneath the entire region.     

Forecasting Hourly Roadside Particulate Matter in Taipei County of Taiwan Based on First‐Order and One‐Variable Grey Model

In this study, seven types of first‐order and one‐variable grey differential equation model (abbreviated as GM (1, 1) model) were used to forecast hourly roadside particulate matter (PM) including PM₁₀ and PM_2.5 concentrations in Taipei County of Taiwan. Their forecasting performance was also compared. The results indicated that the minimum mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and maximum correlation coefficient (R) was 11.70%, 60.06, 7.75, and 0.90%, respectively when forecasting PM₁₀. When forecasting PM_2.5, the minimum MAPE, MSE, RMSE, and maximum R‐value of 16.33%, 29.78, 5.46, and 0.90, respectively could be achieved. All statistical values revealed that the forecasting performance of GM (1, 1, x⁽⁰⁾), GM (1, 1, a), and GM (1, 1, b) outperformed other GM (1, 1) models. According to the results, it revealed that GM (1, 1) was an efficiently early warning tool for providing PM information to the roadside inhabitants.     

Aerosol optical depth retrieval by HJ-1/CCD supported by MODIS surface reflectance data

The high spatial resolution and temporal observation frequency of HJ-1/CCD make it suitable for aerosol monitoring. However, because of the lack of a shortwave infrared band, it is difficult to use HJ-1/CCD imagery to retrieve aerosol optical depth (AOD). We developed a new algorithm for HJ-1/CCD AOD retrieval by introducing MODIS surface reflectance outputs (MOD09) as support. In this algorithm HJ-1/CCD blue band surface reflectance was retrieved through MOD09 blue band surface reflectance by band matching of the two sensors. AOD at 550 nm was then generated through a pre-calculated look-up table for HJ-1/CCD. Eighteen HJ-1/CCD images covering the Jing-Jin-Tang (Beijing-Tianjin-Tangshan) region were used to retrieve AOD using the new algorithm, and the AODs were then validated using AERONET ground measurements in Beijing and Xianghe. The validation shows that compared with AERONET ground measurements, 27/29 AODs have error less than 0.1 in absolute value.

     

Source apportionment and secondary organic aerosol estimation of PM2.5 in an urban atmosphere in China

PM2.5 is the key pollutant in atmospheric pollution in China.With new national air quality standards taking effect,PM2.5 has become a major issue for future pollution control.To effectively prevent and control PM2.5,its emission sources must be precisely and thoroughly understood.However,there are few publications reporting comprehensive and systematic results of PM2.5 source apportionment in the country.Based on PM2.5 sampling during 2009 in Shenzhen and follow-up investigation,positive matrix factorization(PMF)analysis has been carried out to understand the major sources and their temporal and spatial variations.The results show that in urban Shenzhen(University Town site),annual mean PM2.5 concentration was 42.2μg m?3,with secondary sulfate,vehicular emission,biomass burning and secondary nitrate as major sources;these contributed30.0%,26.9%,9.8%and 9.3%to total PM2.5,respectively.Other sources included high chloride,heavy oil combustion,sea salt,dust and the metallurgical industry,with contributions between 2%–4%.Spatiotemporal variations of various sources show that vehicular emission was mainly a local source,whereas secondary sulfate and biomass burning were mostly regional.Secondary nitrate had both local and regional sources.Identification of secondary organic aerosol(SOA)has always been difficult in aerosol source apportionment.In this study,the PMF model and organic carbon/elemental carbon(OC/EC)ratio method were combined to estimate SOA in PM2.5.The results show that in urban Shenzhen,annual SOA mass concentration was 7.5μg m?3,accounting for 57%of total organic matter,with precursors emitted from vehicles as the major source.This work can serve as a case study for further in-depth research on PM2.5 pollution and source apportionment in China.     

Space-time variability of the Amazon River plume based on satellite ocean color

Satellite ocean color images were used to determine the space-time variability of the Amazon River plume from 2000–2004. The relationship between sea-surface salinity (SSS) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) absorption coefficient for dissolved and detrital material (a_dg) (r²=0.76, n=30, rmse=0.4) was used to identify the Amazon River plume low-salinity waters (<34 psu). The plume's spatial information was extracted from satellite bi-weekly time series using two metrics: plume area and plume shape. These metrics identified the seasonal variability of plume dimensions and dispersion patterns. During the study period, the plume showed the largest areas from July to August and the smallest from December to January. The mean annual amplitude and the mean, maximum and minimum plume areas were 1020×10³ km², 680×10³ km², 1506×10³ km² and 268×10³ km², respectively. Three main shapes and dispersion pattern periods were identified: (1) flow to the northeastern South American coast, in a narrow band adjacent to the continental shelf, from January to April; (2) flow to the Caribbean region, from April to July; and (3) flow to the Central Equatorial Atlantic Ocean, from August to December. Cross-correlation techniques were used to quantify the relationship between the plume's spatial variability and environmental forcing factors, including Amazon River discharge, wind field and ocean currents. The results showed that (1) river discharge is the main factor influencing plume area variability, (2) the wind field regulates the plume's northwestward flow velocity and residence time near the river mouth, and (3) surface currents have a strong influence over river plume dispersion patterns.     

High-Frequency Radiation from an Earthquake Fault: A Review and a Hypothesis of Fractal Rupture Front Geometry

Observed high-frequency (HF) radiation from earthquake faults exhibits specific properties that cannot be deduced or extrapolated from low-frequency fault behavior. In particular: (1) HF time functions look like random signals, with smooth mean spectrum and moderately heavy-tailed probability distribution function for amplitudes; (2) well-known directivity of low-frequency radiation related to rupture propagation is strongly reduced at HF, suggesting incoherent (delta-correlated) behavior of the HF radiator, and contradicting the usual picture of a rupture front as a regular, non-fractal moving line; (3) in the spectral domain, HF radiation occupies a certain specific band seen as a plateau on acceleration source spectra $ K(f) = f^{2} \dot{M}_{0} (f) $ . The lower cutoff frequency f _b of K(f) spectra is often located significantly higher than the common spectral corner frequency f _c, or f _a. In many cases, empirical f _b(M ₀) trends are significantly slower as compared to the simple f _b ∝ M ₀ ^?1/3 , testifying the lack of similarity in spectral shapes; (4) evidence is accumulating in support of the reality of the upper cutoff frequency of K(f): fault-controlled f _max, or f _uf. However, its identification is often hampered by such problems as: (a) strong interference between f _uf and site-controlled f _max; (b) possible location of f _uf above the observable spectral range; and (c) substantial deviations of individual source spectra from the ideal spectral shape; (5) intrinsic structure of random-like HF radiation has been shown to bear significant self-similar (fractal) features. A HF signal can be represented as a product of a random HF “carrier signal” with constant mean square amplitude, and a positive modulation function, again random, that represents a signal envelope. It is this modulation function that shows approximately fractal behavior. This kind of behavior was revealed over a broad range of time scales, from 1 to 300 s from teleseismic data and from 0.04 to 30 s from near-fault accelerogram data. To explain in a qualitative way many of these features, it is proposed that rupture propagation can be visualized as occurring, simultaneously, at two different space–time scales. At a macro-scale (i.e. at a low resolution view), one can safely believe in the reality of a singly connected rupture with a front as a smooth line, like a crack tip, that propagates in a locally unilateral way. At a micro-scale, the rupture front is tortuous and disjoint, and can be visualized as a multiply connected fractal “line” or polyline. It propagates, locally, in random directions, and is governed by stochastic regularities, including fractal time structure. The two scales and styles are separated by a certain characteristic time, of the order of (0.07–0.15) × rupture duration. The domain of fractal behavior spans a certain HF frequency range; its boundaries, related to the lower and upper fractal limits, are believed to be manifested as f _b and f _uf.