Governing urban wetlands for green growth in the Western Region Megapolis of Sri Lanka

The Western Region Megapolis (WRM) of Sri Lanka, the conurbation associated with Greater Colombo and covering the entire Western Province, is the thriving economic center of the country. According to the State of Sri Lankan Cities 2018 report (GoSL 2018), the city accounts for 40% of Sri Lanka’s gross domestic product (GDP), 30% of its population and is the nation’s administrative center. The WRM is also endowed with wetlands of international importance. This includes the Bellanwila-Attidiya marshes: a 370-ha freshwater marsh in southern Colombo rich in biodiversity (Box 1); the Colombo Flood Detention Area: a 400-ha network of marshes and canals that traverse the DISCUSSION BRIEF An aerial view of the city of Colombo in Sri Lanka with its network of wetlands supporting urban dwellers - A hub for green growth. Photo: Martin Seemungal city; and the Muthurajawela marsh: a 2,500-ha saltwater marsh in northern Colombo, which is the largest saline peat bog in Sri Lanka (IUCN and CEA 2006). The aim of this brief is to support the efforts of the Government of Sri Lanka to leverage the WRM wetlands to foster green growth. Green growth promotes economic development alongside environmental sustainability, and is gaining traction as a model to achieve sustainable urban development globally (Hammer et al. 2011). According to OECD (2013: 9), governments promote green growth “to create jobs and attract firms and investment, while improving local environmental quality and addressing global environmental challenges, particularly climate change.

Annual report RUAF - Cities farming for the future, South and South East Asia Region, 2008

Urban agriculture / Training / Development projects / India / Sri Lanka

Recommendations for the wise use of urban and peri-urban wetlands in Kolkata, India

Urban and peri-urban wetlands provide services seen and unseen to millions of people and to the environment on which we rely. Wetlands support people’s needs for food, water, fiber, fuel, medicine and livelihoods. Also, they provide vital “ecosystem services” such as purifying water, mitigating floods, absorbing carbon, providing cultural and recreational spaces, and ensuring there are nutrients for farming. However, wetlands face serious risks. They are confronted by a range of threats flowing from modern life, such as urban development, pollution and agricultural use. To protect these crucial areas, this brief offers recommendations on the wise use of urban and peri-urban wetlands in Kolkata, India, as guidance to the Department of Environment in West Bengal and other decision makers. The recommendations are based on two sources. First, a research project carried out in Kolkata from 2012 to 2016 assessed the current status and use of wetlands in peri-urban areas (Figure 1). In this study, we reviewed how communities depend on wetlands for their livelihoods as well as how this dependency is affected by urbanization and the sustainable intensification of agriculture. Second, representatives from more than 10 government and nongovernmental institutions in Kolkata reviewed the results of this study and provided feedback. The final conclusions provide guidance for future policy change for the wise use of wetlands in Kolkata as well as in urban and peri-urban areas elsewhere in India

Ensuring health and food safety from rapidly expanding wastewater irrigation in South Asia: BMZ final report 2005-2008

Wastewater irrigation / Institutions / Public health / Health hazards / Diseases / Cropping systems / Vegetables / Fodder / Livestock / Risk assessment / Economic evaluation / Surveys / GIS / Research priorities / South Asia / India / Pakistan / Hyderabad / Faisalabad / Musi River

Analysis of polymorphisms in the merozoite surface protein-3α gene and two microsatellite loci in Sri Lankan Plasmodium vivax: evidence of population substructure in Sri Lanka.

The geographical distribution of genetic variation in Plasmodium vivax samples (N = 386) from nine districts across Sri Lanka is described using three markers; the P. vivax merozoite surface protein-3α (Pvmsp-3α) gene, and the two microsatellites m1501 and m3502. At Pvmsp-3α, 11 alleles were found with an expected heterozygosity (H(e)) of 0.81, whereas at m1501 and m3502, 24 alleles (H(e) = 0.85) and 8 alleles (H(e) = 0.74) were detected, respectively. Overall, 95 unique three locus genotypes were detected among the 279 samples positive at all three loci (H(e) = 0.95). Calculating the pairwise fixation index (F(ST)) revealed statistically significant population structure. The presence of identical 2-loci microsatellite genotypes in a significant proportion of samples revealed local clusters of closely related isolates contributing to strong linkage disequilibrium between marker alleles. The results show evidence of high genetic diversity and possible population substructure of P. vivax populations in Sri Lanka

Malaria in Sri Lanka: one year post-tsunami

One year ago, the authors of this article reported in this journal on the malaria situation in Sri Lanka prior to the tsunami that hit on 26 December 2004, and estimated the likelihood of a post-tsunami malaria outbreak to be low. Malaria incidence has decreased in 2005 as compared to 2004 in most districts, including the ones that were hit hardest by the tsunami. The malaria incidence (aggregated for the whole country) in 2005 followed the downward trend that started in 2000. However, surveillance was somewhat affected by the tsunami in some coastal areas and the actual incidence in these areas may have been higher than recorded, although there were no indications of this and it is unlikely to have affected the overall trend significantly. The focus of national and international post tsunami malaria control efforts was supply of antimalarials, distribution of impregnated mosquito nets and increased monitoring in the affected area. Internationally donated antimalarials were either redundant or did not comply with national drug policy, however, few seem to have entered circulation outside government control. Despite distribution of mosquito nets, still a large population is relatively exposed to mosquito bites due to inadequate housing. There were no indications of increased malaria vector abundance. Overall it is concluded that the tsunami has not negatively influenced the malaria situation in Sri Lanka

Pre-elimination stage of malaria in Sri Lanka: assessing the level of hidden parasites in the population

<p>Abstract</p> <p>Background</p> <p>With the dramatic drop in the transmission of malaria in Sri Lanka in recent years, the country entered the malaria pre-elimination stage in 2008. Assessing the community prevalence of hidden malaria parasites following several years of extremely low transmission is central to the process of complete elimination. The existence of a parasite reservoir in a population free from clinical manifestations, would influence the strategy for surveillance and control towards complete elimination.</p> <p>Methods</p> <p>The prevalence of hidden parasite reservoirs in two historically malaria endemic districts, Anuradhapura and Kurunegala, previously considered as high malaria transmission areas in Sri Lanka, where peaks of transmission follow the rainy seasons was assessed. Blood samples of non-febrile individuals aged five to 55 years were collected from randomly selected areas in the two districts at community level and a questionnaire was used to collect demographic information and movement of the participants. A simple, highly sensitive nested PCR was carried out to detect both <it>Plasmodium falciparum </it>and <it>Plasmodium vivax</it>, simultaneously.</p> <p>Results</p> <p>In total, 3,023 individuals from 101 villages participated from both districts comprising mostly adults between the ages 19-55 years. Out of these, only about 1.4% of them (n = 19) could recall having had malaria during the past five years. Analysis of a subset of samples (n = 1322) from the two districts using PCR showed that none of the participants had hidden parasites.</p> <p>Discussion</p> <p>A reservoir of hidden parasites is unlikely to be a major concern or a barrier to the ongoing malaria elimination efforts in Sri Lanka. However, as very low numbers of indigenous cases are still recorded, an island-wide assessment and in particular, continued alertness and follow up action are still needed. The findings of this study indicate that any future assessments should be based on an adaptive sampling approach, involving prompt sampling of all subjects within a specified radius, whenever a malaria case is identified in a given focus.</p

Temporal correlation between malaria and rainfall in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Rainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex.</p> <p>Methods</p> <p>The statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression.</p> <p>Results</p> <p>For most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography.</p> <p>Conclusion</p> <p>Seasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.</p

Models for short term malaria prediction in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.</p> <p>Methods</p> <p>Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.</p> <p>Results</p> <p>The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.</p> <p>Conclusion</p> <p>Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.</p