Rural-urban land conversion is currently a common social economic phenomenon during the process of economic development and rural urbanization in China. Rural-urban land conversion is positively effective as far as so...Rural-urban land conversion is currently a common social economic phenomenon during the process of economic development and rural urbanization in China. Rural-urban land conversion is positively effective as far as social and economic benefits are concerned (Yang, 2002), but its negative effect is also evident, resulting in such problems as low efficiency of rural land configuration and loss of social welfare. Consequently, farm-ers should also have an equal chance to enjoy the social welfare enhanced by land conversion. Based on the theories of welfare economy, this paper puts forward policy suggestions by discussing the welfare changes of various interest groups, builds the model of welfare distribution, and analyzes the conditions of maximizing social welfare. The absolute and opposite value of social welfare is closely related with the speed of rural-urban land conversion, and governments should give farmers and collectives fair compensa-tion to make up for the utility loss caused by land expropriation, which are conclusions drawn from this paper. This study aims to provide a theoretical basis for regulating targets and evaluation criteria, realizing the mechanism and implementation of public po-lices during rural-urban land conversion.展开更多
This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order t...This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.展开更多
Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal...Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.展开更多
基金supported by National Natural Science Foundation of China: Welfare Measuring and Balancing of Different Interest Groups during Rural-urban Land Conversion (Grant No. 70773047)Special Fund of Doctoral Disciplines in Ministry of Education of China: Research on Value Choice and Exterior Factors of Rural-urban Land Conversion (Grant No. 20070504020)
文摘Rural-urban land conversion is currently a common social economic phenomenon during the process of economic development and rural urbanization in China. Rural-urban land conversion is positively effective as far as social and economic benefits are concerned (Yang, 2002), but its negative effect is also evident, resulting in such problems as low efficiency of rural land configuration and loss of social welfare. Consequently, farm-ers should also have an equal chance to enjoy the social welfare enhanced by land conversion. Based on the theories of welfare economy, this paper puts forward policy suggestions by discussing the welfare changes of various interest groups, builds the model of welfare distribution, and analyzes the conditions of maximizing social welfare. The absolute and opposite value of social welfare is closely related with the speed of rural-urban land conversion, and governments should give farmers and collectives fair compensa-tion to make up for the utility loss caused by land expropriation, which are conclusions drawn from this paper. This study aims to provide a theoretical basis for regulating targets and evaluation criteria, realizing the mechanism and implementation of public po-lices during rural-urban land conversion.
基金supported by Open Project of State Key Laboratory of Urban Water Resources and Environments, Harbin Institute of Technology (No. QA200902)
文摘This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.
文摘Source apportionment of particulate matter （PM10） measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores （PCA/APCS） and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization （PMF） receptor model to investigate each model＇s source-apportioning capability. All models used the PM10 chemical composition （organic carbon （OC）, elemental carbon （EC）, water soluble inorganic ions （WSIC）, and trace elements） for source apportionment. The average PM10 concentration during the study period was 249.7±103.9 μg/m3 （range： 61.4-584.8 μg/m3）. The UNMIX model resolved five sources （soil dust （SD）, vehicular emissions （VE）, secondary aerosols （SA）, a mixed source of biomass burning （BB） and sea salt （SS）, and industrial emissions （IE））. The PCA/APCS model also resolved five sources, two of which also included mixed sources （SD, VE, SD＋SS, （SA＋BB＋SS） and 1E）. The PMF analysis differentiated seven individual sources （SD, VE, SA, BB, SS, IE, and fossil fuel combustion （FFC））. All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.
